Mande valency

Last update: 2023-10-10

Конструкции главное

Для всех пар языков одно наблюдение на каждый стимул. Здесь одно и то же расстояние по хитмэпу, кластеризации:

heatmap

Code

df |> 
  filter(number %in% to_keep,
         !str_detect(construction, "no equivalent"),
         !is.na(construction_type)) |> 
  arrange(language)  |> 
  distinct(language, number, construction_type) |>  
  pairwise_count(language, number) |> 
  rename(total = n) ->
  df_pairwise_total

df |> 
  filter(number %in% to_keep,
         !str_detect(construction, "no equivalent"),
         !is.na(construction_type)) |> 
  group_by(number) |> 
  arrange(language) |> 
  pairwise_count(language, construction_type)   |> 
  mutate(n = 1) |> 
  group_by(item1, item2) |> 
  reframe(n = sum(n)) -> 
  df_pairwise_within_construction

df_pairwise_total |> 
  left_join(df_pairwise_within_construction) |>  
  mutate(percentage = round(n/total*100, 2),
         item1 = case_match(item1, 
                            "Dan_Gweetaa" ~ "Dan Gweetaa",
                            .default = item1),
         item2 = case_match(item2, 
                            "Dan_Gweetaa" ~ "Dan Gweetaa",
                            .default = item2)) |> 
  select(item1, item2, percentage) |> 
  mutate(item1 = factor(item1, levels = c("Guro", "Dan Gweetaa", "Mano", "Kpelle", "Kono", "Looma", "Bamana")),
         item2 = factor(item2, levels = c("Guro", "Dan Gweetaa", "Mano", "Kpelle", "Kono", "Looma", "Bamana"))) |> 
  ggplot(aes(item1, item2))+
  geom_tile(aes(fill = percentage), colour = "white") +
  geom_text(aes(label = str_c(percentage, "%")), colour = "white") +
  scale_fill_gradient(low = "lightblue", high = "navy")+
  coord_fixed()+
  labs(x = "", y = "", title = "Heatmap with all stimuli") +
  theme(legend.position = "bottom")

hclust

Code

df_pairwise_total |> 
  left_join(df_pairwise_within_construction) |>  
  mutate(percentage = 100-round(n/total*100, 2),
         item1 = case_match(item1, 
                            "Dan_Gweetaa" ~ "Dan Gweetaa",
                            .default = item1),
         item2 = case_match(item2, 
                            "Dan_Gweetaa" ~ "Dan Gweetaa",
                            .default = item2)) |> 
  select(item1, item2, percentage) |> 
  pivot_wider(names_from = item2, values_from = percentage) |> 
  arrange(item1) |> 
  select(-item1) |> 
  as.dist() ->
  dist_gold_standard

library("ape")
dist_gold_standard |>   
  hclust() |> 
  as.phylo() %>% 
  plot(main = "Clusterization with all stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5,
       font = 2)

neighbornet

Code

dist_gold_standard |> 
  neighborNet() |> 
  plot()
title(main = "Heatmap for all stimuli")

Конструкции v2

Все примеры, без фильтрации

Code

df_wide |>
  mutate(across(Guro:Bamana, function(x){ifelse(is.na(x), 0, 1)})) |> 
  select(Guro:Bamana) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo() %>%
  plot(main = "Все примеры, без фильтрации",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5,
       font = 2)

Все примеры, без фильтрации, одно наблюдение на стимул

Code

df |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type)) |> 
  arrange(language)  |> 
  distinct(language, number, construction_type) |>  
  pairwise_count(language, number) |> 
  rename(total = n) ->
  df_pairwise_total

Для всех пар языков одно наблюдение на каждый стимул. Стимулов в каждой паре языков разное количество:

Code

df_pairwise_total |> 
  pivot_wider(names_from = item2, values_from = total) |> 
  select(item1, Dan_Gweetaa, Guro, Kono, Kpelle, Looma, Mano, Bamana) |> 
  knitr::kable()

item1	Dan_Gweetaa	Guro	Kono	Kpelle	Looma	Mano	Bamana
Dan_Gweetaa	NA	94	101	106	95	107	108
Guro	94	NA	99	99	96	102	105
Kono	101	99	NA	112	101	113	113
Kpelle	106	99	112	NA	100	116	115
Looma	95	96	101	100	NA	104	106
Mano	107	102	113	116	104	NA	118
Bamana	108	105	113	115	106	118	NA

Code

df |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type)) |> 
  group_by(number) |> 
  arrange(language) |> 
  pairwise_count(language, construction_type)   |> 
  mutate(n = 1) |> 
  group_by(item1, item2) |> 
  reframe(n = sum(n)) -> 
  df_pairwise_within_construction

df_pairwise_total |> 
  left_join(df_pairwise_within_construction) |>  
  mutate(percentage = 100-round(n/total*100, 2),
         item1 = case_match(item1, 
                            "Dan_Gweetaa" ~ "Dan Gweetaa",
                            .default = item1),
         item2 = case_match(item2, 
                            "Dan_Gweetaa" ~ "Dan Gweetaa",
                            .default = item2)) |> 
  select(item1, item2, percentage) |> 
  pivot_wider(names_from = item2, values_from = percentage) |> 
  arrange(item1) |> 
  select(-item1) |> 
  as.dist() |>   
  hclust() |> 
  as.phylo() %>% 
  plot(main = "Clusterization with all stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5,
       font = 2)

Только стимулы, для которых есть все языки, одно наблюдение на стимул

Code

df |> 
  filter(number %in% to_keep,
         !str_detect(construction, "no equivalent"),
         !is.na(construction_type)) |> 
  arrange(language)  |> 
  distinct(language, number, construction_type) |>  
  pairwise_count(language, number) |> 
  rename(total = n) ->
  df_pairwise_total

Всего 83 стимула в каждом языке:

Code

df |> 
  filter(number %in% to_keep,
         !str_detect(construction, "no equivalent"),
         !is.na(construction_type)) |> 
  group_by(number) |> 
  arrange(language) |> 
  pairwise_count(language, construction_type)   |> 
  mutate(n = 1) |> 
  group_by(item1, item2) |> 
  reframe(n = sum(n)) -> 
  df_pairwise_within_construction

df_pairwise_total |> 
  left_join(df_pairwise_within_construction) |>  
  mutate(percentage = 100-round(n/total*100, 2),
         item1 = case_match(item1, 
                            "Dan_Gweetaa" ~ "Dan Gweetaa",
                            .default = item1),
         item2 = case_match(item2, 
                            "Dan_Gweetaa" ~ "Dan Gweetaa",
                            .default = item2)) |> 
  select(item1, item2, percentage) |> 
  pivot_wider(names_from = item2, values_from = percentage) |> 
  arrange(item1) |> 
  select(-item1) |> 
  as.dist() |>   
  hclust() |> 
  as.phylo() %>% 
  plot(main = "Clusterization with the common of stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5,
       font = 2)

Только стимулы, для которых есть все языки, случайная конструкция для каждого языка на стимул

v1

Code

df |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type),
         number %in% to_keep) |>
  group_by(number, language) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(number, language, construction_type) |> 
  mutate(construction_type2 = 1)  |> 
  pivot_wider(names_from = language, values_from = construction_type2, values_fill = 0) |> 
  select(Bamana:Mano)  |> 
  t() |> 
  dist(method = "binary")  |> 
  hclust(method = "average") |> 
  as.phylo()  %>%
  plot(main = "clusterization with random equivalents and common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v2

Code

df |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type),
         number %in% to_keep) |>
  group_by(number, language) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(number, language, construction_type) |> 
  mutate(construction_type2 = 1)  |> 
  pivot_wider(names_from = language, values_from = construction_type2, values_fill = 0) |> 
  select(Bamana:Mano)  |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "clusterization with random equivalents and common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v3

Code

df |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type),
         number %in% to_keep) |>
  group_by(number, language) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(number, language, construction_type) |> 
  mutate(construction_type2 = 1)  |> 
  pivot_wider(names_from = language, values_from = construction_type2, values_fill = 0) |> 
  select(Bamana:Mano)  |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "clusterization with random equivalents and common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v4

Code

df |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type),
         number %in% to_keep) |>
  group_by(number, language) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(number, language, construction_type) |> 
  mutate(construction_type2 = 1)  |> 
  pivot_wider(names_from = language, values_from = construction_type2, values_fill = 0) |> 
  select(Bamana:Mano)  |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "clusterization with random equivalents and common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v5

Code

df |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type),
         number %in% to_keep) |>
  group_by(number, language) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(number, language, construction_type) |> 
  mutate(construction_type2 = 1)  |> 
  pivot_wider(names_from = language, values_from = construction_type2, values_fill = 0) |> 
  select(Bamana:Mano)  |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "clusterization with random equivalents and common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v6

Code

df |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type),
         number %in% to_keep) |>
  group_by(number, language) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(number, language, construction_type) |> 
  mutate(construction_type2 = 1)  |> 
  pivot_wider(names_from = language, values_from = construction_type2, values_fill = 0) |> 
  select(Bamana:Mano)  |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "clusterization with random equivalents and common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v7

Code

df |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type),
         number %in% to_keep) |>
  group_by(number, language) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(number, language, construction_type) |> 
  mutate(construction_type2 = 1)  |> 
  pivot_wider(names_from = language, values_from = construction_type2, values_fill = 0) |> 
  select(Bamana:Mano)  |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "clusterization with random equivalents and common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v8

Code

df |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type),
         number %in% to_keep) |>
  group_by(number, language) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(number, language, construction_type) |> 
  mutate(construction_type2 = 1)  |> 
  pivot_wider(names_from = language, values_from = construction_type2, values_fill = 0) |> 
  select(Bamana:Mano)  |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "clusterization with random equivalents and common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v9

Code

df |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type),
         number %in% to_keep) |>
  group_by(number, language) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(number, language, construction_type) |> 
  mutate(construction_type2 = 1)  |> 
  pivot_wider(names_from = language, values_from = construction_type2, values_fill = 0) |> 
  select(Bamana:Mano)  |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "clusterization with random equivalents and common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v10

Code

df |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type),
         number %in% to_keep) |>
  group_by(number, language) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(number, language, construction_type) |> 
  mutate(construction_type2 = 1)  |> 
  pivot_wider(names_from = language, values_from = construction_type2, values_fill = 0) |> 
  select(Bamana:Mano)  |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "clusterization with random equivalents and common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v11

Code

df |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type),
         number %in% to_keep) |>
  group_by(number, language) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(number, language, construction_type) |> 
  mutate(construction_type2 = 1)  |> 
  pivot_wider(names_from = language, values_from = construction_type2, values_fill = 0) |> 
  select(Bamana:Mano)  |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "clusterization with random equivalents and common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v12

Code

df |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type),
         number %in% to_keep) |>
  group_by(number, language) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(number, language, construction_type) |> 
  mutate(construction_type2 = 1)  |> 
  pivot_wider(names_from = language, values_from = construction_type2, values_fill = 0) |> 
  select(Bamana:Mano)  |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "clusterization with random equivalents and common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v13

Code

df |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type),
         number %in% to_keep) |>
  group_by(number, language) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(number, language, construction_type) |> 
  mutate(construction_type2 = 1)  |> 
  pivot_wider(names_from = language, values_from = construction_type2, values_fill = 0) |> 
  select(Bamana:Mano)  |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "clusterization with random equivalents and common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v14

Code

df |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type),
         number %in% to_keep) |>
  group_by(number, language) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(number, language, construction_type) |> 
  mutate(construction_type2 = 1)  |> 
  pivot_wider(names_from = language, values_from = construction_type2, values_fill = 0) |> 
  select(Bamana:Mano)  |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "clusterization with random equivalents and common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

Конструкции (old)

Я вынул из таблицы данные по дан, гуро, коно, кпелле, лоома, мано и сделал столбцы

• number
• cunstruction
• exteded_cunstruction
• postposition
• language

Видимо, все эти примеры надо поправить. Сколько у нас наблюдений по каждому языку?

Code

df |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type)) |> 
  count(language)

language	n
Bamana	157
Dan_Gweetaa	134
Guro	124
Kono	115
Kpelle	147
Looma	115
Mano	214

А пока давайте посмотрим на общую таблицу:

Code

df_wide <- readxl::read_xlsx("../GM_merged_wide.xlsx")

Можно посчитать, сколько раз пары языков заполняют один и тот же стимул:

Code

df |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type)) |> 
  arrange(language) |> 
  distinct(language, number) |> 
  pairwise_count(language, number) |> 
  rename(total = n) ->
  df_pairwise_total

df_pairwise_total |> 
  pivot_wider(names_from = item2, values_from = total) |> 
  arrange(item1)

item1	Bamana	Dan_Gweetaa	Guro	Kono	Kpelle	Looma	Mano
Bamana	NA	108	105	113	115	106	118
Dan_Gweetaa	108	NA	94	101	106	95	107
Guro	105	94	NA	99	99	96	102
Kono	113	101	99	NA	112	101	113
Kpelle	115	106	99	112	NA	100	116
Looma	106	95	96	101	100	NA	104
Mano	118	107	102	113	116	104	NA

Можно посчитать, сколько раз пары языков заполняют одну и ту же конструкцию (т. е. совпадают в конструкции, в одном и том же стимуле):

Code

df |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type)) |> 
  group_by(number) |> 
  arrange(language) |> 
  pairwise_count(language, construction_type) |> 
  mutate(n = 1) |> # to unify
  group_by(item1, item2) |> 
  reframe(n = sum(n)) ->
  df_pairwise_within_construction

df_pairwise_within_construction |>
  pivot_wider(names_from = item2, values_from = n) |> 
  arrange(item1) |> 
  select(item1, Dan_Gweetaa, Guro, Kono, Kpelle, Looma, Mano, Bamana) 

item1	Dan_Gweetaa	Guro	Kono	Kpelle	Looma	Mano	Bamana
Bamana	50	54	47	52	54	54	NA
Dan_Gweetaa	NA	61	51	56	56	63	50
Guro	61	NA	52	60	57	65	54
Kono	51	52	NA	89	58	84	47
Kpelle	56	60	89	NA	65	95	52
Looma	56	57	58	65	NA	67	54
Mano	63	65	84	95	67	NA	54

Мне кажется, что чтобы посчитать процент совпадений, о котором просила Маша, нужно разделить последнюю таблицу на предпоследнюю:

Code

df_pairwise_total |> 
  left_join(df_pairwise_within_construction) |> 
  mutate(percentage = round(n/total*100, 3)) |> 
  select(item1, item2, percentage) |> 
  pivot_wider(names_from = item2, values_from = percentage) |> 
  arrange(item1) |> 
  select(item1, Dan_Gweetaa, Guro, Kono, Kpelle, Looma, Mano, Bamana)

item1	Dan_Gweetaa	Guro	Kono	Kpelle	Looma	Mano	Bamana
Bamana	46.296	51.429	41.593	45.217	50.943	45.763	NA
Dan_Gweetaa	NA	64.894	50.495	52.830	58.947	58.879	46.296
Guro	64.894	NA	52.525	60.606	59.375	63.725	51.429
Kono	50.495	52.525	NA	79.464	57.426	74.336	41.593
Kpelle	52.830	60.606	79.464	NA	65.000	81.897	45.217
Looma	58.947	59.375	57.426	65.000	NA	64.423	50.943
Mano	58.879	63.725	74.336	81.897	64.423	NA	45.763

Можно построить тепловую карту:

Code

df_pairwise_total |> 
  left_join(df_pairwise_within_construction) |> 
  mutate(percentage = round(n/total*100, 2),
         item1 = case_match(item1, 
                            "Dan_Gweetaa" ~ "Dan Gweetaa",
                            .default = item1),
         item2 = case_match(item2, 
                            "Dan_Gweetaa" ~ "Dan Gweetaa",
                            .default = item2)) |> 
  select(item1, item2, percentage) |> 
  ggplot(aes(item1, item2))+
  geom_tile(aes(fill = percentage), colour = "white") +
  geom_text(aes(label = str_c(percentage, "%")), colour = "white") +
  scale_fill_gradient(low = "lightblue", high = "navy")+
  coord_fixed()+
  labs(x = "", y = "") +
  theme(legend.position = "bottom")

Теперь можем провести быструю кластеризацию:

hclust

Code

df_wide |>
  mutate(across(Guro:Bamana, function(x){ifelse(is.na(x), 0, 1)})) |> 
  select(Guro:Bamana) |> 
  t() |> 
  dist(method = "binary")  ->
  all_stimuli

all_stimuli |> 
  hclust() |> 
  as.phylo() %>%
  plot(main = "Все примеры, без фильтрации",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5,
       font = 2)

neighbornet

Code

all_stimuli |> 
  neighborNet() |> 
  plot()
title(main = "Все примеры, без фильтрации")

Можно еще посмотреть на кластеризацию конструкций, но это не поместиться на экран. Однако я считаю, что это было бы интересно Сереже.

Вот что будет, если взять только первые строки для каждого языка:

hclust

Code

df |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type)) |>
  group_by(number, language) |> 
  slice(1)  |> 
  ungroup() |> 
  select(number, language, construction_type) |> 
  mutate(construction_type2 = 1) |> 
  pivot_wider(names_from = language, values_from = construction_type2, values_fill = 0) |> 
  select(Bamana:Mano)  |> 
  t() |> 
  dist(method = "binary")  ->
  sample_first_construction

sample_first_construction |> 
  hclust() |> 
  as.phylo() %>%
  plot(main = "Выборка с первыми строками для каждого языка",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5,
       font = 2)

neighbornet

Code

sample_first_construction |> 
  neighborNet() |> 
  plot()
title(main = "Выборка с первыми строками для каждого языка")

МХ: А что будет, если выбрать случайный эквивалент, а не первую строку?

Сегодня (07-10-2023) я вижу следующее:

• Бамана всегда дальше всех
• Группа [Mano [Kono Kpelle]] всегда вместе
• Группа [Dan Gweetaa Guro] всегда вместе
• Единственная вариация проиходит с Looma он встречается в следующих комбинациях:
  • [Dan Gweetaa [Guro Looma]]
  • [[Dan Gweetaa Guro] Looma]
  • [Looma [Mano [Kono Kpelle]]]

hclust

Code

set.seed(42)
df |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type)) |>
  group_by(number, language) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(number, language, construction_type) |> 
  mutate(construction_type2 = 1) |> 
  pivot_wider(names_from = language, values_from = construction_type2, values_fill = 0) |> 
  select(Bamana:Mano)  |> 
  t() |> 
  dist(method = "binary")  ->
  sample_random_construction

sample_random_construction |> 
  hclust() |> 
  as.phylo() %>%
  plot(main = "Clusterization with random equivalents",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

neighbornet

Code

sample_random_construction |> 
  neighborNet() |> 
  plot()
title(main = "Выборка со случайным эквивалентом для каждого языка")

Вот что будет, если взять только популярные конструкции:

hclust

Code

df_wide |> 
  group_by(number) |> 
  filter(n == max(n))  |> 
  ungroup() |> 
  mutate(across(Guro:Bamana, function(x){ifelse(is.na(x), 0, 1)})) |> 
  select(Guro:Bamana) |> 
  t() |> 
  dist(method = "binary")  ->
  sample_popular_construction

sample_popular_construction |> 
  hclust() |> 
  as.phylo() %>%
  plot(main = "Выборка с популярынми конструкциями",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

neighbornet

Code

sample_popular_construction  |> 
  neighborNet() |> 
  plot()
title(main = "Выборка с популярынми конструкциями")

МХ: я также думаю что можно попробовать проанализировать только те стимулы где у ВСЕХ языков есть эквиваленты

Всего стимулов 130, но некоторые из них не попали в датасет: 112, 114, 122, 125, 128

Code

df |> 
  count(number, language) |> 
  count(number) |> 
  filter(n == 7) |> 
  pull(number) ->
  to_keep

Вот список стимулов, для которых есть данные всех семи языков (всего таких случаев 83):

Code

to_keep

 [1]   1   2   3   4   6   7   8   9  11  12  14  16  17  18  19  22  23  25  26
[20]  27  28  29  31  32  33  34  35  36  38  39  40  41  43  44  46  49  50  51
[39]  53  54  55  57  58  60  63  66  67  68  69  70  71  72  74  75  76  77  78
[58]  79  80  81  83  85  89  90  91  93  94  96  98  99 100 101 102 103 105 106
[77] 108 109 110 117 118 119 121

Или наоборот, вот номера, для которых не во всех языках есть данные:

Code

df |> 
  count(number, language) |> 
  count(number) |> 
  filter(n < 7) |> 
  pull(number)

 [1]   5  10  13  15  20  21  24  30  37  42  45  47  48  52  56  59  61  62  64
[20]  65  73  82  84  86  87  88  92  95  97 104 107 111 113 115 116 120 123 124
[39] 126 127 129 130

Запустим кластеризацию:

hclust

Code

df_wide |> 
  filter(number %in% to_keep) |> 
  mutate(across(Guro:Bamana, function(x){ifelse(is.na(x), 0, 1)}))  |> 
  select(Guro:Bamana) |> 
  t() |> 
  dist(method = "binary") ->
  stimuli_sample_all_langs

stimuli_sample_all_langs |> 
  hclust() |> 
  as.phylo() %>%
  plot(main = "Clusterization with common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

neighbornet

Code

stimuli_sample_all_langs  |> 
  neighborNet() |> 
  plot()
title(main = "Выборка стимулов со всеми 6 языками")

heatmap

Code

df |> 
  filter(number %in% to_keep,
         !str_detect(construction, "no equivalent"),
         !is.na(construction_type)) |> 
  arrange(language)  |> 
  distinct(language, number, construction_type)  |> 
  pairwise_count(language, number) |> 
  rename(total = n) ->
  df_pairwise_total

df |> 
  filter(number %in% to_keep,
         !str_detect(construction, "no equivalent"),
         !is.na(construction_type)) |> 
  group_by(number) |> 
  arrange(language) |> 
  pairwise_count(language, construction_type)  |> 
  mutate(n = 1) |> 
  group_by(item1, item2) |> 
  reframe(n = sum(n)) -> 
  df_pairwise_within_construction

df_pairwise_total |> 
  left_join(df_pairwise_within_construction) |>  
  mutate(percentage = round(n/total*100, 2),
         item1 = case_match(item1, 
                            "Dan_Gweetaa" ~ "Dan Gweetaa",
                            .default = item1),
         item2 = case_match(item2, 
                            "Dan_Gweetaa" ~ "Dan Gweetaa",
                            .default = item2)) |> 
  select(item1, item2, percentage) |> 
  ggplot(aes(item1, item2))+
  geom_tile(aes(fill = percentage), colour = "white") +
  geom_text(aes(label = str_c(percentage, "%")), colour = "white") +
  scale_fill_gradient(low = "lightblue", high = "navy")+
  coord_fixed()+
  labs(x = "", y = "", title = "Выборка стимулов со всеми 6 языками") +
  theme(legend.position = "bottom")

Интересно, что выборка со случайным эквивалентом и выборка только тех стимулов, где есть данные по всем шести языкам, дает такие разные результаты. Что будет если взять выборку данных по всем языкам и В НЕЙ провести случайную выборку эквивалентной конструкции?

hclust

Code

df |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type),
         number %in% to_keep) |>
  group_by(number, language) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(number, language, construction_type) |> 
  mutate(construction_type2 = 1) |> 
  pivot_wider(names_from = language, values_from = construction_type2, values_fill = 0) |> 
  select(Bamana:Mano)  |> 
  t() |> 
  dist(method = "binary") ->
  stimuli_sample_all_langs_and_random

stimuli_sample_all_langs_and_random |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "clusterization with random equivalents and common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

neighbornet

Code

stimuli_sample_all_langs_and_random  |> 
  neighborNet() |> 
  plot()
title(main = "Выборка стимулов со всеми 6 языками  (случайный эквивалент)")

Группировка по языкам

Перед тем, как приступить к перекодированию, которое предложила Маша К., посмотрим статистику конструкций по языкам:

Code

df |> 
  count(language, construction_type) |>
  group_by(construction_type) |> 
  mutate(overall = sum(n)) |>  
  pivot_wider(names_from = language, values_from = n, values_fill = 0) |> 
  arrange(desc(overall)) |> 
  select(construction_type, overall, Guro, Dan_Gweetaa, Mano, Kpelle, Kono, Looma, Bamana)

construction_type	overall	Guro	Dan_Gweetaa	Mano	Kpelle	Kono	Looma	Bamana
X Aux Y V	298	44	37	46	36	32	42	61
X Aux V Y Ad	179	23	22	35	23	20	21	35
X Aux Y N V	102	5	11	36	22	12	9	7
X Aux N V Y Ad	68	12	15	10	6	7	12	6
Xgen N Aux V Y Ad	37	8	3	14	5	2	4	1
X Aux V loc	37	6	5	8	7	5	6	0
X Aux Xrefl N V Y Ad	34	4	2	11	8	5	0	4
Y be X Ad	19	2	1	4	4	4	2	2
Ygen N be X Ad	18	0	2	4	5	4	2	1
Xgen N be Y Ad	16	1	1	5	3	2	3	1
Y Aux V X Ad	16	3	2	2	1	2	2	4
X be Y Ad	15	1	1	2	3	4	1	3
Ygen N Aux V X Ad	13	0	2	7	3	1	0	0
X+Y Aux N V	12	0	0	5	5	2	0	0
Ygen N Aux X V	10	0	7	1	1	1	0	0
X+Y Aux V	9	1	0	0	3	1	1	3
X Aux Xrefl V Y Ad	8	1	1	2	1	1	1	1
Y Aux X N V	8	1	2	2	2	0	0	1
X Aux N V	8	0	1	2	2	2	1	0
Y Aux X V	6	0	1	0	0	0	0	5
X Aux Z V Y Ad	5	3	0	1	0	0	0	1
Xgen Y Aux V	4	1	1	1	0	0	1	0
Xgen Y Aux Xrefl V	4	0	0	1	2	1	0	0
X+Y be N Ad	3	0	0	0	1	1	0	1
X Aux 3sg V Y Ad	3	1	1	1	0	0	0	0
Ygen N be.neg X Ad	3	0	0	1	1	1	0	0
X+Y Aux V X+Yrefl Ad	3	0	0	3	0	0	0	0
X Aux V Y N Ad	2	0	0	0	0	0	0	2
X V Aux Y Ad	2	0	0	0	0	0	0	2
X be X Ad	2	0	0	0	0	0	0	2
X be Y N Ad	2	0	1	0	0	0	0	1
Xgen Y Aux X V	2	0	1	0	0	0	0	1
Y Aux Y V	2	0	0	0	0	0	0	2
X Aux Xrefl N V Y N Ad	2	0	2	0	0	0	0	0
X be Y	2	1	1	0	0	0	0	0
Z Aux X V Y	2	1	1	0	0	0	0	0
Xgen N Aux N V Y Ad	2	2	0	0	0	0	0	0
X be.neg Y Ad	2	0	0	1	1	0	0	0
N adj be X Ad Vinf Y Ad	1	0	0	0	0	0	0	1
X Aux V Aux V Y Ad	1	0	0	0	0	0	0	1
X Aux Xrefl SELF V Y Ad	1	0	0	0	0	0	0	1
X Aux Y Ad	1	0	0	0	0	0	0	1
X Aux Y V Refl Ad	1	0	0	0	0	0	0	1
X N V Y	1	0	0	0	0	0	0	1
Xgen N Aux Y Ad	1	0	0	0	0	0	0	1
Y Aux V Aux V X Ad	1	0	0	0	0	0	0	1
Y Aux Xposs N V	1	0	0	0	0	0	0	1
Ygen N Aux X Ad	1	0	0	0	0	0	0	1
N be X Y Ad	1	0	1	0	0	0	0	0
X Aux 3sg N V Y Ad	1	0	1	0	0	0	0	0
X Aux 3sg Z V Y Ad	1	0	1	0	0	0	0	0
X Aux Yposs N V	1	0	1	0	0	0	0	0
X Aux.neg V Y Ad	1	0	1	0	0	0	0	0
Xgen Y N Aux V	1	0	1	0	0	0	0	0
Y Aux V X N Ad N Ad	1	0	1	0	0	0	0	0
Ygen N be X N Ad	1	0	1	0	0	0	0	0
Z Aux V X Y loc	1	0	1	0	0	0	0	0
Z Aux X Y V	1	0	1	0	0	0	0	0
3sg Aux V Y Ad	1	1	0	0	0	0	0	0
Y Aux X N V Xrefl Ad	1	1	0	0	0	0	0	0
Yposs X Aux V	1	1	0	0	0	0	0	0
X Aux Ygen N V	1	0	0	0	0	1	0	0
X Aux.Neg V Y Ad	1	0	0	0	0	1	0	0
X N Aux.Neg V Y Ad	1	0	0	0	0	1	0	0
X+Y Aux V Z Ad	1	0	0	0	0	1	0	0
Y N be.neg X Ad	1	0	0	0	0	1	0	0
X+Y Aux X+Yrefl N V 3sg Refl Ad	1	0	0	0	1	0	0	0
X+Y Aux X+Yrefl recp N V	1	0	0	0	1	0	0	0
X Aux N V Y N Ad	1	0	0	0	0	0	1	0
X N Aux V Y Ad	1	0	0	0	0	0	1	0
X int N Aux V Y Ad	1	0	0	0	0	0	1	0
X+Y Aux Ad N Num	1	0	0	0	0	0	1	0
X+Y Aux N V Z Ad	1	0	0	0	0	0	1	0
X+Y Aux V int Ad	1	0	0	0	0	0	1	0
Xposs Y be.neg V	1	0	0	0	0	0	1	0
3sg N Vger be.neg Y Ad X Ad	1	0	0	1	0	0	0	0
X Aux Y V adj	1	0	0	1	0	0	0	0
X be adj Y Ad	1	0	0	1	0	0	0	0
X+Y Aux V X+Yrefl recp Ad	1	0	0	1	0	0	0	0
X+Y Aux V adv X+Yrefl recp Ad	1	0	0	1	0	0	0	0
X+Y Aux X+Yrefl N V	1	0	0	1	0	0	0	0
X+Y N Aux V	1	0	0	1	0	0	0	0
Xgen N be	1	0	0	1	0	0	0	0
Z Aux 3sg V X Ad Y	1	0	0	1	0	0	0	0

Маша предложила классификацию, которую я попробую воспроизвести.

1. простая переходная вида X Aux Y V (только этот случай)
2. простая непереходная X Aux V Y Ad, X Aux V loc, X+Y Aux V, Y Aux V X Ad (по идее исчерпывающий список, а что я забыла?)
3. конструкция с копулой - можно взять автоматически все случаи, где в формуле есть копула be: Y be X Ad, X be Y Ad, X be adj Y Ad, N be Xgen Y Ad, X+Y be N Ad и т.д.
4. некопульная конструкция со сложным глаголом - взять автоматически все случаи, где в формуле выполняется два условия: есть Aux (=нет be) и есть хотя бы одно N (вместе переходные, и непереходные) - X Aux N V Y Ad; X Aux Y N V; Xgen N Aux V Y Ad и т.д.

По идее это не исчерпывающий список, останется что-то на периферии вроде X+Y Aux V X+Ypron recp Ad, но это редкие штуки.

Code

df |> 
  count(language, construction_type) |>
  mutate(type = case_when(construction_type == "X Aux Y V" ~ "transitive",
                          str_detect(construction_type, "Aux") &
                            !str_detect(construction_type, "N") ~ "intransitive",
                          str_detect(construction_type, "be") ~ "copula",
                          str_detect(construction_type, "Aux") &
                            str_detect(construction_type, "N") ~ "complex verb",
                          TRUE ~ "other")) |> 
  group_by(construction_type) |> 
  mutate(overall = sum(n)) |>  
  pivot_wider(names_from = language, values_from = n, values_fill = 0) |> 
  arrange(desc(overall)) |> 
  select(construction_type, type,   overall, Guro, Dan_Gweetaa, Mano, Kpelle, Kono, Looma, Bamana)

construction_type	type	overall	Guro	Dan_Gweetaa	Mano	Kpelle	Kono	Looma	Bamana
X Aux Y V	transitive	298	44	37	46	36	32	42	61
X Aux V Y Ad	intransitive	179	23	22	35	23	20	21	35
X Aux Y N V	complex verb	102	5	11	36	22	12	9	7
X Aux N V Y Ad	complex verb	68	12	15	10	6	7	12	6
Xgen N Aux V Y Ad	complex verb	37	8	3	14	5	2	4	1
X Aux V loc	intransitive	37	6	5	8	7	5	6	0
X Aux Xrefl N V Y Ad	complex verb	34	4	2	11	8	5	0	4
Y be X Ad	copula	19	2	1	4	4	4	2	2
Ygen N be X Ad	copula	18	0	2	4	5	4	2	1
Xgen N be Y Ad	copula	16	1	1	5	3	2	3	1
Y Aux V X Ad	intransitive	16	3	2	2	1	2	2	4
X be Y Ad	copula	15	1	1	2	3	4	1	3
Ygen N Aux V X Ad	complex verb	13	0	2	7	3	1	0	0
X+Y Aux N V	complex verb	12	0	0	5	5	2	0	0
Ygen N Aux X V	complex verb	10	0	7	1	1	1	0	0
X+Y Aux V	intransitive	9	1	0	0	3	1	1	3
X Aux Xrefl V Y Ad	intransitive	8	1	1	2	1	1	1	1
Y Aux X N V	complex verb	8	1	2	2	2	0	0	1
X Aux N V	complex verb	8	0	1	2	2	2	1	0
Y Aux X V	intransitive	6	0	1	0	0	0	0	5
X Aux Z V Y Ad	intransitive	5	3	0	1	0	0	0	1
Xgen Y Aux V	intransitive	4	1	1	1	0	0	1	0
Xgen Y Aux Xrefl V	intransitive	4	0	0	1	2	1	0	0
X+Y be N Ad	copula	3	0	0	0	1	1	0	1
X Aux 3sg V Y Ad	intransitive	3	1	1	1	0	0	0	0
Ygen N be.neg X Ad	copula	3	0	0	1	1	1	0	0
X+Y Aux V X+Yrefl Ad	intransitive	3	0	0	3	0	0	0	0
X Aux V Y N Ad	complex verb	2	0	0	0	0	0	0	2
X V Aux Y Ad	intransitive	2	0	0	0	0	0	0	2
X be X Ad	copula	2	0	0	0	0	0	0	2
X be Y N Ad	copula	2	0	1	0	0	0	0	1
Xgen Y Aux X V	intransitive	2	0	1	0	0	0	0	1
Y Aux Y V	intransitive	2	0	0	0	0	0	0	2
X Aux Xrefl N V Y N Ad	complex verb	2	0	2	0	0	0	0	0
X be Y	copula	2	1	1	0	0	0	0	0
Z Aux X V Y	intransitive	2	1	1	0	0	0	0	0
Xgen N Aux N V Y Ad	complex verb	2	2	0	0	0	0	0	0
X be.neg Y Ad	copula	2	0	0	1	1	0	0	0
N adj be X Ad Vinf Y Ad	copula	1	0	0	0	0	0	0	1
X Aux V Aux V Y Ad	intransitive	1	0	0	0	0	0	0	1
X Aux Xrefl SELF V Y Ad	intransitive	1	0	0	0	0	0	0	1
X Aux Y Ad	intransitive	1	0	0	0	0	0	0	1
X Aux Y V Refl Ad	intransitive	1	0	0	0	0	0	0	1
X N V Y	other	1	0	0	0	0	0	0	1
Xgen N Aux Y Ad	complex verb	1	0	0	0	0	0	0	1
Y Aux V Aux V X Ad	intransitive	1	0	0	0	0	0	0	1
Y Aux Xposs N V	complex verb	1	0	0	0	0	0	0	1
Ygen N Aux X Ad	complex verb	1	0	0	0	0	0	0	1
N be X Y Ad	copula	1	0	1	0	0	0	0	0
X Aux 3sg N V Y Ad	complex verb	1	0	1	0	0	0	0	0
X Aux 3sg Z V Y Ad	intransitive	1	0	1	0	0	0	0	0
X Aux Yposs N V	complex verb	1	0	1	0	0	0	0	0
X Aux.neg V Y Ad	intransitive	1	0	1	0	0	0	0	0
Xgen Y N Aux V	complex verb	1	0	1	0	0	0	0	0
Y Aux V X N Ad N Ad	complex verb	1	0	1	0	0	0	0	0
Ygen N be X N Ad	copula	1	0	1	0	0	0	0	0
Z Aux V X Y loc	intransitive	1	0	1	0	0	0	0	0
Z Aux X Y V	intransitive	1	0	1	0	0	0	0	0
3sg Aux V Y Ad	intransitive	1	1	0	0	0	0	0	0
Y Aux X N V Xrefl Ad	complex verb	1	1	0	0	0	0	0	0
Yposs X Aux V	intransitive	1	1	0	0	0	0	0	0
X Aux Ygen N V	complex verb	1	0	0	0	0	1	0	0
X Aux.Neg V Y Ad	complex verb	1	0	0	0	0	1	0	0
X N Aux.Neg V Y Ad	complex verb	1	0	0	0	0	1	0	0
X+Y Aux V Z Ad	intransitive	1	0	0	0	0	1	0	0
Y N be.neg X Ad	copula	1	0	0	0	0	1	0	0
X+Y Aux X+Yrefl N V 3sg Refl Ad	complex verb	1	0	0	0	1	0	0	0
X+Y Aux X+Yrefl recp N V	complex verb	1	0	0	0	1	0	0	0
X Aux N V Y N Ad	complex verb	1	0	0	0	0	0	1	0
X N Aux V Y Ad	complex verb	1	0	0	0	0	0	1	0
X int N Aux V Y Ad	complex verb	1	0	0	0	0	0	1	0
X+Y Aux Ad N Num	complex verb	1	0	0	0	0	0	1	0
X+Y Aux N V Z Ad	complex verb	1	0	0	0	0	0	1	0
X+Y Aux V int Ad	intransitive	1	0	0	0	0	0	1	0
Xposs Y be.neg V	copula	1	0	0	0	0	0	1	0
3sg N Vger be.neg Y Ad X Ad	copula	1	0	0	1	0	0	0	0
X Aux Y V adj	intransitive	1	0	0	1	0	0	0	0
X be adj Y Ad	copula	1	0	0	1	0	0	0	0
X+Y Aux V X+Yrefl recp Ad	intransitive	1	0	0	1	0	0	0	0
X+Y Aux V adv X+Yrefl recp Ad	intransitive	1	0	0	1	0	0	0	0
X+Y Aux X+Yrefl N V	complex verb	1	0	0	1	0	0	0	0
X+Y N Aux V	complex verb	1	0	0	1	0	0	0	0
Xgen N be	copula	1	0	0	1	0	0	0	0
Z Aux 3sg V X Ad Y	intransitive	1	0	0	1	0	0	0	0

Вот, что получилось. Маш, что исправить? Вот саммари:

Code

df |> 
  mutate(type = case_when(construction_type == "X Aux Y V" ~ "transitive",
                          str_detect(construction_type, "Aux") &
                            !str_detect(construction_type, "N") ~ "intransitive",
                          str_detect(construction_type, "be") ~ "copula",
                          str_detect(construction_type, "Aux") &
                            str_detect(construction_type, "N") ~ "complex verb",
                          TRUE ~ "other")) |> 
  count(type, language) |> 
  pivot_wider(names_from = language, values_from = n, values_fill = 0)  |> 
  arrange(-Looma) |> 
  select(type, Guro, Dan_Gweetaa, Mano, Kpelle, Kono, Looma, Bamana)

type	Guro	Dan_Gweetaa	Mano	Kpelle	Kono	Looma	Bamana
transitive	44	37	46	36	32	42	61
intransitive	42	39	58	37	31	33	59
complex verb	33	49	90	56	35	31	24
copula	5	9	20	18	17	9	12
other	0	0	0	0	0	0	1

Во-вторых, есть отдельная просьба про распределение конструкций и глаголов. В первом листе stimuli_general в столбце B есть семантическая разметка глаголов. Добавь её, пожалуйста, в глагольную таблицу, которую ты будешь снова генерировать. А дальше я бы попросила сделать следующее. Посчитать для каждого глагола, сколько ему в наших языках соответствует разных конструкций из 4 типов, выделенных выше. А потом слить глаголы из одной семантической группы и посчитать для семантических групп вместе статистику по конструкциям. Грубо говоря я хочу проверить, верно ли, что у глаголов вида effect будет больше переходных конструкций, а у feeling больше сложных глаголов, т.к. более абстрактные значения языки склонны выражать метафорически.

Code

df |> 
  left_join(stimuli) |> 
    mutate(type = case_when(construction_type == "X Aux Y V" ~ "transitive",
                          str_detect(construction_type, "Aux") &
                            !str_detect(construction_type, "N") ~ "intransitive",
                          str_detect(construction_type, "be") ~ "copula",
                          str_detect(construction_type, "Aux") &
                            str_detect(construction_type, "N") ~ "complex verb",
                          TRUE ~ "other")) |> 
  count(type, language, semantic_type) |> 
  mutate(language = str_replace(language, "_", " "),
         language = factor(language, levels = c("Guro", "Dan Gweetaa", "Mano", "Kpelle", "Kono", "Looma", "Bamana"))) |> 
  ggplot(aes(n, type))+
  geom_col()+
  facet_grid(semantic_type~language, scales = "free")

Ну вот примерно, то, что ты имела в виду, наверное, но это все нужно пересчитать, после того, как мы утвердим тип каждой из конструкции.

Адлоги

Какие адлоги есть для каждого языка?

Code

df |>
  mutate(adposition = str_split(adposition, ", ")) |> 
  unnest_longer(adposition) |> 
  count(language, adposition) |> 
  na.omit()

language	adposition	n
Bamana	bólo	5
Bamana	fɛ̀	7
Bamana	kàn	5
Bamana	kɔ́	4
Bamana	kɔ́nɔ	1
Bamana	kɔ́rɔ	1
Bamana	lá	29
Bamana	mà	5
Bamana	nɔ̀fɛ̀	2
Bamana	yé	6
Bamana	ɲɛ́	1
Dan_Gweetaa	bhàa	2
Dan_Gweetaa	bhȁ	23
Dan_Gweetaa	dhi̋ɤ	3
Dan_Gweetaa	dhɛ̏	2
Dan_Gweetaa	gɔ̏	5
Dan_Gweetaa	gɯ́	5
Dan_Gweetaa	ká	14
Dan_Gweetaa	kèŋ̏	1
Dan_Gweetaa	loc	6
Dan_Gweetaa	pi̋ɤ	2
Dan_Gweetaa	tȁ	4
Dan_Gweetaa	zɯ̏	1
Guro	jì	2
Guro	la	2
Guro	leè	3
Guro	loc	6
Guro	lɛ	8
Guro	ta	5
Guro	và	8
Guro	ya̰	9
Guro	zì	2
Guro	zuo	2
Guro	ɓa̰	22
Kono	à	11
Kono	hù	5
Kono	lá	1
Kono	loc	5
Kono	mà	28
Kono	mɛ̌i	1
Kono	pòò	1
Kono	pɔ̀	4
Kono	yêi	4
Kpelle	à	12
Kpelle	hù	3
Kpelle	lá	1
Kpelle	loc	7
Kpelle	púlû	4
Kpelle	pɔ́	3
Kpelle	yêi	4
Kpelle	ɓà	33
Kpelle	ɲá	1
Looma	bà	7
Looma	bù	1
Looma	bɛ̀	5
Looma	gà	10
Looma	kómá	1
Looma	loc	6
Looma	mà	19
Looma	pòlù	2
Looma	sù	3
Looma	tá	2
Looma	yà	3
Mano	bà	5
Mano	gé	1
Mano	gɛ̀nɛ̀	1
Mano	ká	24
Mano	kɛ̀lɛ̀	6
Mano	là	7
Mano	loc	8
Mano	lɛ̀ɛ̄	6
Mano	mɔ̀	45
Mano	píé	7
Mano	yí	1
Mano	ŋwɛ́ŋ̀	4

Какие были раньше проблемы:

• в коно нет адлогов
• в дан гвета есть адлоги dhi̋ɤ и dhiɤ̋, bhȁ и bhàa, которые, наверное, про одно и то же. Кроме того есть адлог loc.
• в гуро есть адлоги léè и leè, lɛ̄ и lɛ, ɓa̰ и ɓā̰. Кроме того есть адлог loc.
• в лома есть адлоги mà и mà̀ (с двумя знаками тона). Кроме того есть адлог loc.
• в мано есть адлоги píé и píé (я не понимаю в чем разница…).

Проблемы на 15.02.2023:

• В гуро ɓa̰ и ɓā̰ — разное?
• В гуро ya̰ и yā̰ — разное?

Я не совсем понял, что написала МХ, так что я пока посчитал вот такое вот. Какие адлоги из разных языков встречаются друг с другом в одной расширеной конструкции внутри одного стимула?

Code

df |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type),
         !is.na(adposition)) |> 
  group_by(number) |> 
  mutate(adposition = str_c(language, "__", adposition)) |> 
  pairwise_count(adposition, construction_type)  |>
  group_by(item1, item2) |> 
  reframe(n = sum(n)) |> 
  arrange(desc(n))

item1	item2	n
Kpelle__ɓà	Mano__mɔ̀	22
Mano__mɔ̀	Kpelle__ɓà	22
Kono__mà	Kpelle__ɓà	16
Kpelle__ɓà	Kono__mà	16
Kono__mà	Mano__mɔ̀	15
Mano__mɔ̀	Kono__mà	15
Kono__à	Kpelle__à	11
Kpelle__à	Kono__à	11
Dan_Gweetaa__bhȁ	Kpelle__ɓà	7
Dan_Gweetaa__bhȁ	Mano__mɔ̀	7
Kono__à	Mano__ká	7
Kpelle__à	Mano__ká	7
Kpelle__ɓà	Dan_Gweetaa__bhȁ	7
Mano__ká	Kono__à	7
Mano__ká	Kpelle__à	7
Mano__mɔ̀	Dan_Gweetaa__bhȁ	7
Bamana__lá	Kpelle__ɓà	6
Guro__ɓa̰	Kpelle__ɓà	6
Kpelle__ɓà	Bamana__lá	6
Kpelle__ɓà	Guro__ɓa̰	6
Bamana__lá	Mano__mɔ̀	5
Dan_Gweetaa__loc	Guro__loc	5
Dan_Gweetaa__loc	Kono__loc	5
Dan_Gweetaa__loc	Kpelle__loc	5
Dan_Gweetaa__loc	Looma__loc	5
Dan_Gweetaa__loc	Mano__loc	5
Guro__loc	Dan_Gweetaa__loc	5
Guro__loc	Kono__loc	5
Guro__loc	Kpelle__loc	5
Guro__loc	Looma__loc	5
Guro__loc	Mano__loc	5
Guro__ɓa̰	Kono__mà	5
Guro__ɓa̰	Mano__mɔ̀	5
Kono__loc	Dan_Gweetaa__loc	5
Kono__loc	Guro__loc	5
Kono__loc	Kpelle__loc	5
Kono__loc	Looma__loc	5
Kono__loc	Mano__loc	5
Kono__mà	Guro__ɓa̰	5
Kono__mà	Looma__mà	5
Kpelle__loc	Dan_Gweetaa__loc	5
Kpelle__loc	Guro__loc	5
Kpelle__loc	Kono__loc	5
Kpelle__loc	Looma__loc	5
Kpelle__loc	Mano__loc	5
Kpelle__ɓà	Looma__mà	5
Looma__loc	Dan_Gweetaa__loc	5
Looma__loc	Guro__loc	5
Looma__loc	Kono__loc	5
Looma__loc	Kpelle__loc	5
Looma__loc	Mano__loc	5
Looma__mà	Kono__mà	5
Looma__mà	Kpelle__ɓà	5
Looma__mà	Mano__mɔ̀	5
Mano__loc	Dan_Gweetaa__loc	5
Mano__loc	Guro__loc	5
Mano__loc	Kono__loc	5
Mano__loc	Kpelle__loc	5
Mano__loc	Looma__loc	5
Mano__mɔ̀	Bamana__lá	5
Mano__mɔ̀	Guro__ɓa̰	5
Mano__mɔ̀	Looma__mà	5
Bamana__lá	Guro__ɓa̰	4
Dan_Gweetaa__bhȁ	Kono__mà	4
Dan_Gweetaa__ká	Guro__ɓa̰	4
Guro__ɓa̰	Bamana__lá	4
Guro__ɓa̰	Dan_Gweetaa__ká	4
Kono__mà	Dan_Gweetaa__bhȁ	4
Kono__yêi	Kpelle__yêi	4
Kpelle__yêi	Kono__yêi	4
Bamana__lá	Dan_Gweetaa__bhȁ	3
Bamana__lá	Dan_Gweetaa__ká	3
Bamana__lá	Kono__mà	3
Bamana__lá	Looma__mà	3
Dan_Gweetaa__bhȁ	Bamana__lá	3
Dan_Gweetaa__ká	Bamana__lá	3
Dan_Gweetaa__ká	Guro__ya̰	3
Guro__lɛ	Kpelle__ɓà	3
Guro__và	Kpelle__ɓà	3
Guro__và	Looma__mà	3
Guro__và	Mano__mɔ̀	3
Guro__ya̰	Dan_Gweetaa__ká	3
Guro__ya̰	Kono__à	3
Guro__ya̰	Kpelle__à	3
Kono__à	Guro__ya̰	3
Kono__mà	Bamana__lá	3
Kono__mà	Looma__bà	3
Kono__yêi	Mano__kɛ̀lɛ̀	3
Kpelle__à	Guro__ya̰	3
Kpelle__yêi	Mano__kɛ̀lɛ̀	3
Kpelle__ɓà	Guro__lɛ	3
Kpelle__ɓà	Guro__và	3
Kpelle__ɓà	Looma__bà	3
Kpelle__ɓà	Mano__ká	3
Looma__bà	Kono__mà	3
Looma__bà	Kpelle__ɓà	3
Looma__bà	Mano__mɔ̀	3
Looma__mà	Bamana__lá	3
Looma__mà	Guro__và	3
Mano__ká	Kpelle__ɓà	3
Mano__kɛ̀lɛ̀	Kono__yêi	3
Mano__kɛ̀lɛ̀	Kpelle__yêi	3
Mano__mɔ̀	Guro__và	3
Mano__mɔ̀	Looma__bà	3
Bamana__bólo	Kono__yêi	2
Bamana__bólo	Kpelle__yêi	2
Bamana__fɛ̀	Mano__píé	2
Bamana__kɔ́	Guro__zuo	2
Bamana__kɔ́	Kpelle__púlû	2
Bamana__lá	Guro__và	2
Bamana__lá	Kono__hù	2
Bamana__lá	Kpelle__hù	2
Bamana__lá	Looma__bà	2
Bamana__lá	Mano__ká	2
Bamana__yé	Guro__lɛ	2
Bamana__yé	Looma__bɛ̀	2
Dan_Gweetaa__bhȁ	Guro__ta	2
Dan_Gweetaa__bhȁ	Guro__và	2
Dan_Gweetaa__gɔ̏	Guro__leè	2
Dan_Gweetaa__gɯ́	Kono__mà	2
Dan_Gweetaa__gɯ́	Kpelle__ɓà	2
Dan_Gweetaa__gɯ́	Looma__mà	2
Dan_Gweetaa__gɯ́	Mano__mɔ̀	2
Dan_Gweetaa__ká	Guro__và	2
Dan_Gweetaa__ká	Kono__à	2
Dan_Gweetaa__ká	Kono__mà	2
Dan_Gweetaa__ká	Kpelle__à	2
Dan_Gweetaa__ká	Mano__ká	2
Dan_Gweetaa__ká	Mano__mɔ̀	2
Dan_Gweetaa__tȁ	Guro__ta	2
Guro__leè	Dan_Gweetaa__gɔ̏	2
Guro__lɛ	Bamana__yé	2
Guro__lɛ	Mano__lɛ̀ɛ̄	2
Guro__lɛ	Mano__mɔ̀	2
Guro__ta	Dan_Gweetaa__bhȁ	2
Guro__ta	Dan_Gweetaa__tȁ	2
Guro__ta	Kono__mà	2
Guro__ta	Kpelle__ɓà	2
Guro__ta	Mano__mɔ̀	2
Guro__và	Bamana__lá	2
Guro__và	Dan_Gweetaa__bhȁ	2
Guro__và	Dan_Gweetaa__ká	2
Guro__và	Kpelle__pɔ́	2
Guro__và	Mano__píé	2
Guro__ya̰	Mano__ká	2
Guro__zuo	Bamana__kɔ́	2
Guro__zuo	Kpelle__púlû	2
Guro__ɓa̰	Looma__gà	2
Guro__ɓa̰	Looma__mà	2
Kono__à	Dan_Gweetaa__ká	2
Kono__à	Looma__gà	2
Kono__hù	Bamana__lá	2
Kono__hù	Kpelle__hù	2
Kono__mà	Dan_Gweetaa__gɯ́	2
Kono__mà	Dan_Gweetaa__ká	2
Kono__mà	Guro__ta	2
Kono__mà	Mano__ká	2
Kono__yêi	Bamana__bólo	2
Kono__yêi	Looma__yà	2
Kpelle__à	Dan_Gweetaa__ká	2
Kpelle__à	Looma__gà	2
Kpelle__hù	Bamana__lá	2
Kpelle__hù	Kono__hù	2
Kpelle__púlû	Bamana__kɔ́	2
Kpelle__púlû	Guro__zuo	2
Kpelle__púlû	Mano__píé	2
Kpelle__pɔ́	Guro__và	2
Kpelle__pɔ́	Mano__píé	2
Kpelle__yêi	Bamana__bólo	2
Kpelle__yêi	Looma__yà	2
Kpelle__ɓà	Dan_Gweetaa__gɯ́	2
Kpelle__ɓà	Guro__ta	2
Kpelle__ɓà	Looma__gà	2
Looma__bà	Bamana__lá	2
Looma__bɛ̀	Bamana__yé	2
Looma__gà	Guro__ɓa̰	2
Looma__gà	Kono__à	2
Looma__gà	Kpelle__à	2
Looma__gà	Kpelle__ɓà	2
Looma__gà	Mano__ká	2
Looma__mà	Dan_Gweetaa__gɯ́	2
Looma__mà	Guro__ɓa̰	2
Looma__mà	Looma__sù	2
Looma__sù	Looma__mà	2
Looma__yà	Kono__yêi	2
Looma__yà	Kpelle__yêi	2
Looma__yà	Mano__kɛ̀lɛ̀	2
Mano__ká	Bamana__lá	2
Mano__ká	Dan_Gweetaa__ká	2
Mano__ká	Guro__ya̰	2
Mano__ká	Kono__mà	2
Mano__ká	Looma__gà	2
Mano__kɛ̀lɛ̀	Looma__yà	2
Mano__lɛ̀ɛ̄	Guro__lɛ	2
Mano__mɔ̀	Dan_Gweetaa__gɯ́	2
Mano__mɔ̀	Dan_Gweetaa__ká	2
Mano__mɔ̀	Guro__lɛ	2
Mano__mɔ̀	Guro__ta	2
Mano__píé	Bamana__fɛ̀	2
Mano__píé	Guro__và	2
Mano__píé	Kpelle__púlû	2
Mano__píé	Kpelle__pɔ́	2
Mano__píé	Mano__ŋwɛ́ŋ̀	2
Mano__ŋwɛ́ŋ̀	Mano__píé	2
Bamana__bólo	Bamana__fɛ̀	1
Bamana__bólo	Looma__yà	1
Bamana__bólo	Mano__kɛ̀lɛ̀	1
Bamana__fɛ̀	Bamana__bólo	1
Bamana__fɛ̀	Dan_Gweetaa__bhȁ	1
Bamana__fɛ̀	Dan_Gweetaa__pi̋ɤ	1
Bamana__fɛ̀	Guro__và	1
Bamana__fɛ̀	Kono__pɔ̀	1
Bamana__fɛ̀	Kpelle__pɔ́	1
Bamana__fɛ̀	Kpelle__ɓà	1
Bamana__fɛ̀	Looma__bà	1
Bamana__fɛ̀	Looma__tá	1
Bamana__fɛ̀	Mano__lɛ̀ɛ̄	1
Bamana__fɛ̀	Mano__mɔ̀	1
Bamana__kàn	Bamana__kɔ́	1
Bamana__kàn	Bamana__lá	1
Bamana__kàn	Bamana__mà	1
Bamana__kàn	Bamana__nɔ̀fɛ̀	1
Bamana__kàn	Dan_Gweetaa__kèŋ̏	1
Bamana__kàn	Dan_Gweetaa__tȁ	1
Bamana__kàn	Guro__ta	1
Bamana__kàn	Guro__zuo	1
Bamana__kàn	Kono__mà	1
Bamana__kàn	Kpelle__púlû	1
Bamana__kàn	Kpelle__ɓà	1
Bamana__kàn	Looma__pòlù	1
Bamana__kàn	Mano__mɔ̀	1
Bamana__kɔ́	Bamana__kàn	1
Bamana__kɔ́	Bamana__lá	1
Bamana__kɔ́	Bamana__nɔ̀fɛ̀	1
Bamana__kɔ́	Dan_Gweetaa__kèŋ̏	1
Bamana__kɔ́	Kono__mà	1
Bamana__kɔ́	Kono__pòò	1
Bamana__kɔ́	Kpelle__ɓà	1
Bamana__kɔ́	Looma__mà	1
Bamana__kɔ́	Looma__pòlù	1
Bamana__kɔ́	Mano__mɔ̀	1
Bamana__kɔ́	Mano__píé	1
Bamana__lá	Bamana__kàn	1
Bamana__lá	Bamana__kɔ́	1
Bamana__lá	Bamana__nɔ̀fɛ̀	1
Bamana__lá	Dan_Gweetaa__bhàa	1
Bamana__lá	Dan_Gweetaa__kèŋ̏	1
Bamana__lá	Guro__lɛ	1
Bamana__lá	Guro__ta	1
Bamana__lá	Guro__ya̰	1
Bamana__lá	Guro__zuo	1
Bamana__lá	Kono__mɛ̌i	1
Bamana__lá	Kpelle__púlû	1
Bamana__lá	Looma__bù	1
Bamana__lá	Looma__gà	1
Bamana__lá	Looma__pòlù	1
Bamana__lá	Mano__bà	1
Bamana__lá	Mano__là	1
Bamana__lá	Mano__yí	1
Bamana__mà	Bamana__kàn	1
Bamana__mà	Dan_Gweetaa__tȁ	1
Bamana__mà	Guro__ta	1
Bamana__mà	Kono__mà	1
Bamana__mà	Kpelle__ɓà	1
Bamana__mà	Mano__mɔ̀	1
Bamana__nɔ̀fɛ̀	Bamana__kàn	1
Bamana__nɔ̀fɛ̀	Bamana__kɔ́	1
Bamana__nɔ̀fɛ̀	Bamana__lá	1
Bamana__nɔ̀fɛ̀	Dan_Gweetaa__bhȁ	1
Bamana__nɔ̀fɛ̀	Dan_Gweetaa__kèŋ̏	1
Bamana__nɔ̀fɛ̀	Guro__zuo	1
Bamana__nɔ̀fɛ̀	Kono__mà	1
Bamana__nɔ̀fɛ̀	Kpelle__púlû	1
Bamana__nɔ̀fɛ̀	Kpelle__ɓà	1
Bamana__nɔ̀fɛ̀	Looma__bɛ̀	1
Bamana__nɔ̀fɛ̀	Looma__pòlù	1
Bamana__nɔ̀fɛ̀	Mano__mɔ̀	1
Bamana__yé	Dan_Gweetaa__dhi̋ɤ	1
Bamana__yé	Looma__gà	1
Bamana__yé	Mano__lɛ̀ɛ̄	1
Bamana__ɲɛ́	Dan_Gweetaa__gɔ̏	1
Bamana__ɲɛ́	Guro__leè	1
Bamana__ɲɛ́	Kono__mà	1
Bamana__ɲɛ́	Kpelle__ɓà	1
Bamana__ɲɛ́	Looma__bà	1
Bamana__ɲɛ́	Mano__ká	1
Bamana__ɲɛ́	Mano__lɛ̀ɛ̄	1
Dan_Gweetaa__bhàa	Bamana__lá	1
Dan_Gweetaa__bhàa	Kono__hù	1
Dan_Gweetaa__bhàa	Kpelle__hù	1
Dan_Gweetaa__bhàa	Looma__bù	1
Dan_Gweetaa__bhàa	Mano__bà	1
Dan_Gweetaa__bhȁ	Bamana__fɛ̀	1
Dan_Gweetaa__bhȁ	Bamana__nɔ̀fɛ̀	1
Dan_Gweetaa__bhȁ	Dan_Gweetaa__tȁ	1
Dan_Gweetaa__bhȁ	Guro__ya̰	1
Dan_Gweetaa__bhȁ	Guro__zì	1
Dan_Gweetaa__bhȁ	Guro__ɓa̰	1
Dan_Gweetaa__bhȁ	Kono__à	1
Dan_Gweetaa__bhȁ	Kono__hù	1
Dan_Gweetaa__bhȁ	Kpelle__à	1
Dan_Gweetaa__bhȁ	Looma__bà	1
Dan_Gweetaa__bhȁ	Looma__bɛ̀	1
Dan_Gweetaa__bhȁ	Looma__gà	1
Dan_Gweetaa__bhȁ	Looma__mà	1
Dan_Gweetaa__bhȁ	Looma__sù	1
Dan_Gweetaa__bhȁ	Mano__bà	1
Dan_Gweetaa__dhi̋ɤ	Bamana__yé	1
Dan_Gweetaa__dhɛ̏	Mano__lɛ̀ɛ̄	1
Dan_Gweetaa__gɔ̏	Bamana__ɲɛ́	1
Dan_Gweetaa__gɔ̏	Kono__mà	1
Dan_Gweetaa__gɔ̏	Kono__yêi	1
Dan_Gweetaa__gɔ̏	Kpelle__yêi	1
Dan_Gweetaa__gɔ̏	Kpelle__ɓà	1
Dan_Gweetaa__gɔ̏	Looma__bà	1
Dan_Gweetaa__gɔ̏	Looma__yà	1
Dan_Gweetaa__gɔ̏	Mano__gɛ̀nɛ̀	1
Dan_Gweetaa__gɔ̏	Mano__ká	1
Dan_Gweetaa__gɔ̏	Mano__kɛ̀lɛ̀	1
Dan_Gweetaa__gɔ̏	Mano__lɛ̀ɛ̄	1
Dan_Gweetaa__gɯ́	Guro__và	1
Dan_Gweetaa__gɯ́	Guro__ɓa̰	1
Dan_Gweetaa__gɯ́	Looma__sù	1
Dan_Gweetaa__gɯ́	Mano__píé	1
Dan_Gweetaa__gɯ́	Mano__ŋwɛ́ŋ̀	1
Dan_Gweetaa__ká	Dan_Gweetaa__tȁ	1
Dan_Gweetaa__ká	Looma__bà	1
Dan_Gweetaa__ká	Looma__gà	1
Dan_Gweetaa__ká	Looma__tá	1
Dan_Gweetaa__kèŋ̏	Bamana__kàn	1
Dan_Gweetaa__kèŋ̏	Bamana__kɔ́	1
Dan_Gweetaa__kèŋ̏	Bamana__lá	1
Dan_Gweetaa__kèŋ̏	Bamana__nɔ̀fɛ̀	1
Dan_Gweetaa__kèŋ̏	Guro__zuo	1
Dan_Gweetaa__kèŋ̏	Kpelle__púlû	1
Dan_Gweetaa__kèŋ̏	Looma__pòlù	1
Dan_Gweetaa__pi̋ɤ	Bamana__fɛ̀	1
Dan_Gweetaa__pi̋ɤ	Guro__và	1
Dan_Gweetaa__pi̋ɤ	Kono__pɔ̀	1
Dan_Gweetaa__pi̋ɤ	Kpelle__pɔ́	1
Dan_Gweetaa__pi̋ɤ	Mano__píé	1
Dan_Gweetaa__tȁ	Bamana__kàn	1
Dan_Gweetaa__tȁ	Bamana__mà	1
Dan_Gweetaa__tȁ	Dan_Gweetaa__bhȁ	1
Dan_Gweetaa__tȁ	Dan_Gweetaa__ká	1
Dan_Gweetaa__tȁ	Guro__jì	1
Dan_Gweetaa__tȁ	Kono__hù	1
Dan_Gweetaa__tȁ	Kono__mà	1
Dan_Gweetaa__tȁ	Kpelle__ɓà	1
Dan_Gweetaa__tȁ	Looma__mà	1
Dan_Gweetaa__tȁ	Mano__mɔ̀	1
Dan_Gweetaa__zɯ̏	Guro__zì	1
Dan_Gweetaa__zɯ̏	Kono__mà	1
Dan_Gweetaa__zɯ̏	Looma__mà	1
Dan_Gweetaa__zɯ̏	Mano__mɔ̀	1
Guro__jì	Dan_Gweetaa__tȁ	1
Guro__jì	Looma__mà	1
Guro__leè	Bamana__ɲɛ́	1
Guro__leè	Kono__mà	1
Guro__leè	Kono__yêi	1
Guro__leè	Kpelle__yêi	1
Guro__leè	Kpelle__ɓà	1
Guro__leè	Looma__bà	1
Guro__leè	Looma__yà	1
Guro__leè	Mano__ká	1
Guro__leè	Mano__kɛ̀lɛ̀	1
Guro__leè	Mano__lɛ̀ɛ̄	1
Guro__lɛ	Bamana__lá	1
Guro__lɛ	Kono__mà	1
Guro__lɛ	Kono__mɛ̌i	1
Guro__lɛ	Looma__bɛ̀	1
Guro__lɛ	Mano__là	1
Guro__ta	Bamana__kàn	1
Guro__ta	Bamana__lá	1
Guro__ta	Bamana__mà	1
Guro__ta	Guro__ɓa̰	1
Guro__ta	Kono__hù	1
Guro__ta	Looma__bà	1
Guro__ta	Mano__là	1
Guro__và	Bamana__fɛ̀	1
Guro__và	Dan_Gweetaa__gɯ́	1
Guro__và	Dan_Gweetaa__pi̋ɤ	1
Guro__và	Guro__ya̰	1
Guro__và	Guro__ɓa̰	1
Guro__và	Kono__mà	1
Guro__và	Kono__pɔ̀	1
Guro__và	Looma__bà	1
Guro__và	Mano__ŋwɛ́ŋ̀	1
Guro__ya̰	Bamana__lá	1
Guro__ya̰	Dan_Gweetaa__bhȁ	1
Guro__ya̰	Guro__và	1
Guro__ya̰	Looma__gà	1
Guro__ya̰	Looma__tá	1
Guro__ya̰	Mano__mɔ̀	1
Guro__zì	Dan_Gweetaa__bhȁ	1
Guro__zì	Dan_Gweetaa__zɯ̏	1
Guro__zì	Kono__mà	1
Guro__zì	Looma__mà	1
Guro__zì	Mano__mɔ̀	1
Guro__zuo	Bamana__kàn	1
Guro__zuo	Bamana__lá	1
Guro__zuo	Bamana__nɔ̀fɛ̀	1
Guro__zuo	Dan_Gweetaa__kèŋ̏	1
Guro__zuo	Kono__pòò	1
Guro__zuo	Looma__pòlù	1
Guro__zuo	Mano__píé	1
Guro__ɓa̰	Dan_Gweetaa__bhȁ	1
Guro__ɓa̰	Dan_Gweetaa__gɯ́	1
Guro__ɓa̰	Guro__ta	1
Guro__ɓa̰	Guro__và	1
Guro__ɓa̰	Looma__bà	1
Guro__ɓa̰	Looma__kómá	1
Guro__ɓa̰	Mano__bà	1
Guro__ɓa̰	Mano__ká	1
Guro__ɓa̰	Mano__là	1
Guro__ɓa̰	Mano__píé	1
Guro__ɓa̰	Mano__ŋwɛ́ŋ̀	1
Kono__à	Dan_Gweetaa__bhȁ	1
Kono__à	Looma__tá	1
Kono__à	Mano__mɔ̀	1
Kono__hù	Dan_Gweetaa__bhàa	1
Kono__hù	Dan_Gweetaa__bhȁ	1
Kono__hù	Dan_Gweetaa__tȁ	1
Kono__hù	Guro__ta	1
Kono__hù	Looma__bù	1
Kono__hù	Looma__mà	1
Kono__hù	Mano__bà	1
Kono__hù	Mano__ká	1
Kono__hù	Mano__yí	1
Kono__lá	Kpelle__lá	1
Kono__lá	Looma__yà	1
Kono__lá	Mano__là	1
Kono__mà	Bamana__kàn	1
Kono__mà	Bamana__kɔ́	1
Kono__mà	Bamana__mà	1
Kono__mà	Bamana__nɔ̀fɛ̀	1
Kono__mà	Bamana__ɲɛ́	1
Kono__mà	Dan_Gweetaa__gɔ̏	1
Kono__mà	Dan_Gweetaa__tȁ	1
Kono__mà	Dan_Gweetaa__zɯ̏	1
Kono__mà	Guro__leè	1
Kono__mà	Guro__lɛ	1
Kono__mà	Guro__và	1
Kono__mà	Guro__zì	1
Kono__mà	Kpelle__hù	1
Kono__mà	Looma__bɛ̀	1
Kono__mà	Looma__gà	1
Kono__mà	Mano__bà	1
Kono__mà	Mano__lɛ̀ɛ̄	1
Kono__mà	Mano__píé	1
Kono__mà	Mano__ŋwɛ́ŋ̀	1
Kono__mɛ̌i	Bamana__lá	1
Kono__mɛ̌i	Guro__lɛ	1
Kono__mɛ̌i	Kpelle__ɓà	1
Kono__mɛ̌i	Mano__là	1
Kono__pòò	Bamana__kɔ́	1
Kono__pòò	Guro__zuo	1
Kono__pòò	Kpelle__púlû	1
Kono__pòò	Mano__píé	1
Kono__pɔ̀	Bamana__fɛ̀	1
Kono__pɔ̀	Dan_Gweetaa__pi̋ɤ	1
Kono__pɔ̀	Guro__và	1
Kono__pɔ̀	Kpelle__pɔ́	1
Kono__pɔ̀	Mano__mɔ̀	1
Kono__pɔ̀	Mano__píé	1
Kono__yêi	Dan_Gweetaa__gɔ̏	1
Kono__yêi	Guro__leè	1
Kpelle__à	Dan_Gweetaa__bhȁ	1
Kpelle__à	Looma__tá	1
Kpelle__à	Mano__mɔ̀	1
Kpelle__hù	Dan_Gweetaa__bhàa	1
Kpelle__hù	Kono__mà	1
Kpelle__hù	Looma__bù	1
Kpelle__hù	Looma__mà	1
Kpelle__hù	Mano__bà	1
Kpelle__hù	Mano__ká	1
Kpelle__hù	Mano__yí	1
Kpelle__lá	Kono__lá	1
Kpelle__lá	Looma__yà	1
Kpelle__lá	Mano__là	1
Kpelle__púlû	Bamana__kàn	1
Kpelle__púlû	Bamana__lá	1
Kpelle__púlû	Bamana__nɔ̀fɛ̀	1
Kpelle__púlû	Dan_Gweetaa__kèŋ̏	1
Kpelle__púlû	Kono__pòò	1
Kpelle__púlû	Looma__pòlù	1
Kpelle__pɔ́	Bamana__fɛ̀	1
Kpelle__pɔ́	Dan_Gweetaa__pi̋ɤ	1
Kpelle__pɔ́	Kono__pɔ̀	1
Kpelle__pɔ́	Looma__mà	1
Kpelle__yêi	Dan_Gweetaa__gɔ̏	1
Kpelle__yêi	Guro__leè	1
Kpelle__ɓà	Bamana__fɛ̀	1
Kpelle__ɓà	Bamana__kàn	1
Kpelle__ɓà	Bamana__kɔ́	1
Kpelle__ɓà	Bamana__mà	1
Kpelle__ɓà	Bamana__nɔ̀fɛ̀	1
Kpelle__ɓà	Bamana__ɲɛ́	1
Kpelle__ɓà	Dan_Gweetaa__gɔ̏	1
Kpelle__ɓà	Dan_Gweetaa__tȁ	1
Kpelle__ɓà	Guro__leè	1
Kpelle__ɓà	Kono__mɛ̌i	1
Kpelle__ɓà	Looma__bɛ̀	1
Kpelle__ɓà	Mano__bà	1
Kpelle__ɓà	Mano__kɛ̀lɛ̀	1
Kpelle__ɓà	Mano__là	1
Kpelle__ɓà	Mano__lɛ̀ɛ̄	1
Kpelle__ɓà	Mano__píé	1
Kpelle__ɓà	Mano__ŋwɛ́ŋ̀	1
Looma__bà	Bamana__fɛ̀	1
Looma__bà	Bamana__ɲɛ́	1
Looma__bà	Dan_Gweetaa__bhȁ	1
Looma__bà	Dan_Gweetaa__gɔ̏	1
Looma__bà	Dan_Gweetaa__ká	1
Looma__bà	Guro__leè	1
Looma__bà	Guro__ta	1
Looma__bà	Guro__và	1
Looma__bà	Guro__ɓa̰	1
Looma__bà	Mano__ká	1
Looma__bà	Mano__lɛ̀ɛ̄	1
Looma__bù	Bamana__lá	1
Looma__bù	Dan_Gweetaa__bhàa	1
Looma__bù	Kono__hù	1
Looma__bù	Kpelle__hù	1
Looma__bù	Mano__bà	1
Looma__bɛ̀	Bamana__nɔ̀fɛ̀	1
Looma__bɛ̀	Dan_Gweetaa__bhȁ	1
Looma__bɛ̀	Guro__lɛ	1
Looma__bɛ̀	Kono__mà	1
Looma__bɛ̀	Kpelle__ɓà	1
Looma__bɛ̀	Mano__ká	1
Looma__bɛ̀	Mano__mɔ̀	1
Looma__gà	Bamana__lá	1
Looma__gà	Bamana__yé	1
Looma__gà	Dan_Gweetaa__bhȁ	1
Looma__gà	Dan_Gweetaa__ká	1
Looma__gà	Guro__ya̰	1
Looma__gà	Kono__mà	1
Looma__gà	Mano__bà	1
Looma__kómá	Guro__ɓa̰	1
Looma__mà	Bamana__kɔ́	1
Looma__mà	Dan_Gweetaa__bhȁ	1
Looma__mà	Dan_Gweetaa__tȁ	1
Looma__mà	Dan_Gweetaa__zɯ̏	1
Looma__mà	Guro__jì	1
Looma__mà	Guro__zì	1
Looma__mà	Kono__hù	1
Looma__mà	Kpelle__hù	1
Looma__mà	Kpelle__pɔ́	1
Looma__mà	Mano__ká	1
Looma__mà	Mano__kɛ̀lɛ̀	1
Looma__mà	Mano__píé	1
Looma__mà	Mano__yí	1
Looma__mà	Mano__ŋwɛ́ŋ̀	1
Looma__pòlù	Bamana__kàn	1
Looma__pòlù	Bamana__kɔ́	1
Looma__pòlù	Bamana__lá	1
Looma__pòlù	Bamana__nɔ̀fɛ̀	1
Looma__pòlù	Dan_Gweetaa__kèŋ̏	1
Looma__pòlù	Guro__zuo	1
Looma__pòlù	Kpelle__púlû	1
Looma__sù	Dan_Gweetaa__bhȁ	1
Looma__sù	Dan_Gweetaa__gɯ́	1
Looma__tá	Bamana__fɛ̀	1
Looma__tá	Dan_Gweetaa__ká	1
Looma__tá	Guro__ya̰	1
Looma__tá	Kono__à	1
Looma__tá	Kpelle__à	1
Looma__tá	Mano__ká	1
Looma__tá	Mano__lɛ̀ɛ̄	1
Looma__tá	Mano__píé	1
Looma__yà	Bamana__bólo	1
Looma__yà	Dan_Gweetaa__gɔ̏	1
Looma__yà	Guro__leè	1
Looma__yà	Kono__lá	1
Looma__yà	Kpelle__lá	1
Looma__yà	Mano__là	1
Mano__bà	Bamana__lá	1
Mano__bà	Dan_Gweetaa__bhàa	1
Mano__bà	Dan_Gweetaa__bhȁ	1
Mano__bà	Guro__ɓa̰	1
Mano__bà	Kono__hù	1
Mano__bà	Kono__mà	1
Mano__bà	Kpelle__hù	1
Mano__bà	Kpelle__ɓà	1
Mano__bà	Looma__bù	1
Mano__bà	Looma__gà	1
Mano__gɛ̀nɛ̀	Dan_Gweetaa__gɔ̏	1
Mano__ká	Bamana__ɲɛ́	1
Mano__ká	Dan_Gweetaa__gɔ̏	1
Mano__ká	Guro__leè	1
Mano__ká	Guro__ɓa̰	1
Mano__ká	Kono__hù	1
Mano__ká	Kpelle__hù	1
Mano__ká	Looma__bà	1
Mano__ká	Looma__bɛ̀	1
Mano__ká	Looma__mà	1
Mano__ká	Looma__tá	1
Mano__ká	Mano__kɛ̀lɛ̀	1
Mano__ká	Mano__lɛ̀ɛ̄	1
Mano__ká	Mano__yí	1
Mano__ká	Mano__ŋwɛ́ŋ̀	1
Mano__kɛ̀lɛ̀	Bamana__bólo	1
Mano__kɛ̀lɛ̀	Dan_Gweetaa__gɔ̏	1
Mano__kɛ̀lɛ̀	Guro__leè	1
Mano__kɛ̀lɛ̀	Kpelle__ɓà	1
Mano__kɛ̀lɛ̀	Looma__mà	1
Mano__kɛ̀lɛ̀	Mano__ká	1
Mano__là	Bamana__lá	1
Mano__là	Guro__lɛ	1
Mano__là	Guro__ta	1
Mano__là	Guro__ɓa̰	1
Mano__là	Kono__lá	1
Mano__là	Kono__mɛ̌i	1
Mano__là	Kpelle__lá	1
Mano__là	Kpelle__ɓà	1
Mano__là	Looma__yà	1
Mano__lɛ̀ɛ̄	Bamana__fɛ̀	1
Mano__lɛ̀ɛ̄	Bamana__yé	1
Mano__lɛ̀ɛ̄	Bamana__ɲɛ́	1
Mano__lɛ̀ɛ̄	Dan_Gweetaa__dhɛ̏	1
Mano__lɛ̀ɛ̄	Dan_Gweetaa__gɔ̏	1
Mano__lɛ̀ɛ̄	Guro__leè	1
Mano__lɛ̀ɛ̄	Kono__mà	1
Mano__lɛ̀ɛ̄	Kpelle__ɓà	1
Mano__lɛ̀ɛ̄	Looma__bà	1
Mano__lɛ̀ɛ̄	Looma__tá	1
Mano__lɛ̀ɛ̄	Mano__ká	1
Mano__lɛ̀ɛ̄	Mano__píé	1
Mano__mɔ̀	Bamana__fɛ̀	1
Mano__mɔ̀	Bamana__kàn	1
Mano__mɔ̀	Bamana__kɔ́	1
Mano__mɔ̀	Bamana__mà	1
Mano__mɔ̀	Bamana__nɔ̀fɛ̀	1
Mano__mɔ̀	Dan_Gweetaa__tȁ	1
Mano__mɔ̀	Dan_Gweetaa__zɯ̏	1
Mano__mɔ̀	Guro__ya̰	1
Mano__mɔ̀	Guro__zì	1
Mano__mɔ̀	Kono__à	1
Mano__mɔ̀	Kono__pɔ̀	1
Mano__mɔ̀	Kpelle__à	1
Mano__mɔ̀	Looma__bɛ̀	1
Mano__mɔ̀	Mano__píé	1
Mano__mɔ̀	Mano__ŋwɛ́ŋ̀	1
Mano__píé	Bamana__kɔ́	1
Mano__píé	Dan_Gweetaa__gɯ́	1
Mano__píé	Dan_Gweetaa__pi̋ɤ	1
Mano__píé	Guro__zuo	1
Mano__píé	Guro__ɓa̰	1
Mano__píé	Kono__mà	1
Mano__píé	Kono__pòò	1
Mano__píé	Kono__pɔ̀	1
Mano__píé	Kpelle__ɓà	1
Mano__píé	Looma__mà	1
Mano__píé	Looma__tá	1
Mano__píé	Mano__lɛ̀ɛ̄	1
Mano__píé	Mano__mɔ̀	1
Mano__yí	Bamana__lá	1
Mano__yí	Kono__hù	1
Mano__yí	Kpelle__hù	1
Mano__yí	Looma__mà	1
Mano__yí	Mano__ká	1
Mano__ŋwɛ́ŋ̀	Dan_Gweetaa__gɯ́	1
Mano__ŋwɛ́ŋ̀	Guro__và	1
Mano__ŋwɛ́ŋ̀	Guro__ɓa̰	1
Mano__ŋwɛ́ŋ̀	Kono__mà	1
Mano__ŋwɛ́ŋ̀	Kpelle__ɓà	1
Mano__ŋwɛ́ŋ̀	Looma__mà	1
Mano__ŋwɛ́ŋ̀	Mano__ká	1
Mano__ŋwɛ́ŋ̀	Mano__mɔ̀	1

Я попробовал сделать таблицу, но она получается огромная… Возьмем только примеры, где больше одного:

Code

df |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type),
         !is.na(adposition)) |>  
  group_by(number) |> 
  mutate(adposition = str_c(language, "__", adposition)) |> 
  pairwise_count(adposition, construction_type) |> 
  group_by(item1, item2) |> 
  reframe(n = sum(n)) |> 
  filter(n > 1) |> 
  mutate(item1 = fct_reorder(item1, n, .desc = TRUE),
         item2 = fct_reorder(item2, n, .desc = TRUE)) |> 
  ggplot(aes(item1, item2))+
  geom_tile(aes(fill = n), colour = "white") +
  geom_text(aes(label = n), colour = "white") +
  scale_fill_gradient(low = "tomato", high = "darkred")+
  coord_fixed()+
  labs(x = "", y = "") +
  theme(legend.position = "bottom",
        axis.text.x = element_text(angle = 90, vjust = 0, hjust=1))

Вот так я понял Машину идеи про то, как посмотреть соответствия между языками, берите лупу:

Code

df |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type),
         !is.na(adposition)) |>  
  group_by(number) |> 
  mutate(adposition = str_c(language, "__", adposition)) |> 
  pairwise_count(adposition, construction_type) |> 
  group_by(item1, item2) |> 
  reframe(n = sum(n)) |> 
  filter(n > 1) |> 
  separate(item1, into = c("language1", "adposition1"), sep = "__") |> 
  separate(item2, into = c("language2", "adposition2"), sep = "__") |> 
  group_by(language1, language2, adposition1) |> 
  reframe(ratio = n/sum(n),
            n = n,
            adposition2 = adposition2) |> 
  distinct() |> 
  mutate(language1 = str_replace(language1, "_", " "),
         language1 = factor(language1, levels = c("Guro", "Dan Gweetaa", "Mano", "Kpelle", "Kono", "Looma", "Bamana")),
         language2 = str_replace(language2, "_", " "),
         language2 = factor(language2, levels = c("Guro", "Dan Gweetaa", "Mano", "Kpelle", "Kono", "Looma", "Bamana"))) |> 
  ggplot(aes(ratio, adposition1, label = str_c(adposition2, "\n",
                                               round(ratio*100), "% (",
                                               n, ")")))+
  geom_col(color = "white")+
  geom_label(position = position_stack(vjust = .5), size = 2)+
  facet_grid(language1~language2, scales = "free")+
  labs(y = "")

Глагольная этимология v2

Все примеры, без фильтрации

Code

et <- readxl::read_xlsx("../GM_merged_wide_verb_MK_0927.xlsx")
et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |>
  filter(value != "0") |> 
  distinct() |> 
  mutate(value = str_replace_all(value, "able, be", "able||| be"),
         value = str_replace_all(value, "bear, give", "bear||| give"),
         value = str_replace_all(value, ", shut", "||| shut"),
         value = str_replace_all(value, "cry, weep", "cry||| weep"),
         value = str_replace_all(value, "shoot, sting", "shoot||| sting"),
         value = str_replace_all(value, "stuck, get", "stuck||| get"),
         value = str_replace_all(value, ",", ";"),
         value = str_replace_all(value, "\\|\\|\\|", ","),
         value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1) |>  
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  select(ends_with("etym")) |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Verb etimology (all stimuli)",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

Только стимулы, для которых есть все языки

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  filter(value != "0",
         number %in% to_keep) |> 
  distinct() |> 
  mutate(value = str_replace_all(value, "able, be", "able||| be"),
         value = str_replace_all(value, "bear, give", "bear||| give"),
         value = str_replace_all(value, ", shut", "||| shut"),
         value = str_replace_all(value, "cry, weep", "cry||| weep"),
         value = str_replace_all(value, "shoot, sting", "shoot||| sting"),
         value = str_replace_all(value, "stuck, get", "stuck||| get"),
         value = str_replace_all(value, ",", ";"),
         value = str_replace_all(value, "\\|\\|\\|", ","),
         value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1) |>  
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  select(ends_with("etym")) |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Verb etimology with common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

Только стимулы, для которых есть все языки, случайная этимология для каждого языка

v1

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |>
  filter(value != "0",
         number %in% to_keep) |>
  group_by(number, predicate_eng, source) |> 
  slice_sample(n = 1)  |> 
  distinct() |> 
  ungroup() |> 
  mutate(value = str_replace_all(value, "able, be", "able||| be"),
         value = str_replace_all(value, "bear, give", "bear||| give"),
         value = str_replace_all(value, ", shut", "||| shut"),
         value = str_replace_all(value, "cry, weep", "cry||| weep"),
         value = str_replace_all(value, "shoot, sting", "shoot||| sting"),
         value = str_replace_all(value, "stuck, get", "stuck||| get"),
         value = str_replace_all(value, ",", ";"),
         value = str_replace_all(value, "\\|\\|\\|", ","),
         value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1) |>  
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  select(ends_with("etym")) |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Verb etimology with random equivalents",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v2

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |>
  filter(value != "0",
         number %in% to_keep) |>
  group_by(number, predicate_eng, source) |> 
  slice_sample(n = 1)  |> 
  distinct() |> 
  ungroup() |> 
  mutate(value = str_replace_all(value, "able, be", "able||| be"),
         value = str_replace_all(value, "bear, give", "bear||| give"),
         value = str_replace_all(value, ", shut", "||| shut"),
         value = str_replace_all(value, "cry, weep", "cry||| weep"),
         value = str_replace_all(value, "shoot, sting", "shoot||| sting"),
         value = str_replace_all(value, "stuck, get", "stuck||| get"),
         value = str_replace_all(value, ",", ";"),
         value = str_replace_all(value, "\\|\\|\\|", ","),
         value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1) |>  
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  select(ends_with("etym")) |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Verb etimology with random equivalents",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v3

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |>
  filter(value != "0",
         number %in% to_keep) |>
  group_by(number, predicate_eng, source) |> 
  slice_sample(n = 1)  |> 
  distinct() |> 
  ungroup() |> 
  mutate(value = str_replace_all(value, "able, be", "able||| be"),
         value = str_replace_all(value, "bear, give", "bear||| give"),
         value = str_replace_all(value, ", shut", "||| shut"),
         value = str_replace_all(value, "cry, weep", "cry||| weep"),
         value = str_replace_all(value, "shoot, sting", "shoot||| sting"),
         value = str_replace_all(value, "stuck, get", "stuck||| get"),
         value = str_replace_all(value, ",", ";"),
         value = str_replace_all(value, "\\|\\|\\|", ","),
         value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1) |>  
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  select(ends_with("etym")) |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Verb etimology with random equivalents",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v4

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |>
  filter(value != "0",
         number %in% to_keep) |>
  group_by(number, predicate_eng, source) |> 
  slice_sample(n = 1)  |> 
  distinct() |> 
  ungroup() |> 
  mutate(value = str_replace_all(value, "able, be", "able||| be"),
         value = str_replace_all(value, "bear, give", "bear||| give"),
         value = str_replace_all(value, ", shut", "||| shut"),
         value = str_replace_all(value, "cry, weep", "cry||| weep"),
         value = str_replace_all(value, "shoot, sting", "shoot||| sting"),
         value = str_replace_all(value, "stuck, get", "stuck||| get"),
         value = str_replace_all(value, ",", ";"),
         value = str_replace_all(value, "\\|\\|\\|", ","),
         value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1) |>  
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  select(ends_with("etym")) |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Verb etimology with random equivalents",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v5

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |>
  filter(value != "0",
         number %in% to_keep) |>
  group_by(number, predicate_eng, source) |> 
  slice_sample(n = 1)  |> 
  distinct() |> 
  ungroup() |> 
  mutate(value = str_replace_all(value, "able, be", "able||| be"),
         value = str_replace_all(value, "bear, give", "bear||| give"),
         value = str_replace_all(value, ", shut", "||| shut"),
         value = str_replace_all(value, "cry, weep", "cry||| weep"),
         value = str_replace_all(value, "shoot, sting", "shoot||| sting"),
         value = str_replace_all(value, "stuck, get", "stuck||| get"),
         value = str_replace_all(value, ",", ";"),
         value = str_replace_all(value, "\\|\\|\\|", ","),
         value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1) |>  
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  select(ends_with("etym")) |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Verb etimology with random equivalents",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v6

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |>
  filter(value != "0",
         number %in% to_keep) |>
  group_by(number, predicate_eng, source) |> 
  slice_sample(n = 1)  |> 
  distinct() |> 
  ungroup() |> 
  mutate(value = str_replace_all(value, "able, be", "able||| be"),
         value = str_replace_all(value, "bear, give", "bear||| give"),
         value = str_replace_all(value, ", shut", "||| shut"),
         value = str_replace_all(value, "cry, weep", "cry||| weep"),
         value = str_replace_all(value, "shoot, sting", "shoot||| sting"),
         value = str_replace_all(value, "stuck, get", "stuck||| get"),
         value = str_replace_all(value, ",", ";"),
         value = str_replace_all(value, "\\|\\|\\|", ","),
         value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1) |>  
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  select(ends_with("etym")) |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Verb etimology with random equivalents",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v7

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |>
  filter(value != "0",
         number %in% to_keep) |>
  group_by(number, predicate_eng, source) |> 
  slice_sample(n = 1)  |> 
  distinct() |> 
  ungroup() |> 
  mutate(value = str_replace_all(value, "able, be", "able||| be"),
         value = str_replace_all(value, "bear, give", "bear||| give"),
         value = str_replace_all(value, ", shut", "||| shut"),
         value = str_replace_all(value, "cry, weep", "cry||| weep"),
         value = str_replace_all(value, "shoot, sting", "shoot||| sting"),
         value = str_replace_all(value, "stuck, get", "stuck||| get"),
         value = str_replace_all(value, ",", ";"),
         value = str_replace_all(value, "\\|\\|\\|", ","),
         value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1) |>  
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  select(ends_with("etym")) |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Verb etimology with random equivalents",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v8

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |>
  filter(value != "0",
         number %in% to_keep) |>
  group_by(number, predicate_eng, source) |> 
  slice_sample(n = 1)  |> 
  distinct() |> 
  ungroup() |> 
  mutate(value = str_replace_all(value, "able, be", "able||| be"),
         value = str_replace_all(value, "bear, give", "bear||| give"),
         value = str_replace_all(value, ", shut", "||| shut"),
         value = str_replace_all(value, "cry, weep", "cry||| weep"),
         value = str_replace_all(value, "shoot, sting", "shoot||| sting"),
         value = str_replace_all(value, "stuck, get", "stuck||| get"),
         value = str_replace_all(value, ",", ";"),
         value = str_replace_all(value, "\\|\\|\\|", ","),
         value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1) |>  
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  select(ends_with("etym")) |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Verb etimology with random equivalents",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v9

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |>
  filter(value != "0",
         number %in% to_keep) |>
  group_by(number, predicate_eng, source) |> 
  slice_sample(n = 1)  |> 
  distinct() |> 
  ungroup() |> 
  mutate(value = str_replace_all(value, "able, be", "able||| be"),
         value = str_replace_all(value, "bear, give", "bear||| give"),
         value = str_replace_all(value, ", shut", "||| shut"),
         value = str_replace_all(value, "cry, weep", "cry||| weep"),
         value = str_replace_all(value, "shoot, sting", "shoot||| sting"),
         value = str_replace_all(value, "stuck, get", "stuck||| get"),
         value = str_replace_all(value, ",", ";"),
         value = str_replace_all(value, "\\|\\|\\|", ","),
         value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1) |>  
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  select(ends_with("etym")) |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Verb etimology with random equivalents",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v10

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |>
  filter(value != "0",
         number %in% to_keep) |>
  group_by(number, predicate_eng, source) |> 
  slice_sample(n = 1)  |> 
  distinct() |> 
  ungroup() |> 
  mutate(value = str_replace_all(value, "able, be", "able||| be"),
         value = str_replace_all(value, "bear, give", "bear||| give"),
         value = str_replace_all(value, ", shut", "||| shut"),
         value = str_replace_all(value, "cry, weep", "cry||| weep"),
         value = str_replace_all(value, "shoot, sting", "shoot||| sting"),
         value = str_replace_all(value, "stuck, get", "stuck||| get"),
         value = str_replace_all(value, ",", ";"),
         value = str_replace_all(value, "\\|\\|\\|", ","),
         value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1) |>  
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  select(ends_with("etym")) |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Verb etimology with random equivalents",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v11

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |>
  filter(value != "0",
         number %in% to_keep) |>
  group_by(number, predicate_eng, source) |> 
  slice_sample(n = 1)  |> 
  distinct() |> 
  ungroup() |> 
  mutate(value = str_replace_all(value, "able, be", "able||| be"),
         value = str_replace_all(value, "bear, give", "bear||| give"),
         value = str_replace_all(value, ", shut", "||| shut"),
         value = str_replace_all(value, "cry, weep", "cry||| weep"),
         value = str_replace_all(value, "shoot, sting", "shoot||| sting"),
         value = str_replace_all(value, "stuck, get", "stuck||| get"),
         value = str_replace_all(value, ",", ";"),
         value = str_replace_all(value, "\\|\\|\\|", ","),
         value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1) |>  
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  select(ends_with("etym")) |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Verb etimology with random equivalents",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v12

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |>
  filter(value != "0",
         number %in% to_keep) |>
  group_by(number, predicate_eng, source) |> 
  slice_sample(n = 1)  |> 
  distinct() |> 
  ungroup() |> 
  mutate(value = str_replace_all(value, "able, be", "able||| be"),
         value = str_replace_all(value, "bear, give", "bear||| give"),
         value = str_replace_all(value, ", shut", "||| shut"),
         value = str_replace_all(value, "cry, weep", "cry||| weep"),
         value = str_replace_all(value, "shoot, sting", "shoot||| sting"),
         value = str_replace_all(value, "stuck, get", "stuck||| get"),
         value = str_replace_all(value, ",", ";"),
         value = str_replace_all(value, "\\|\\|\\|", ","),
         value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1) |>  
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  select(ends_with("etym")) |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Verb etimology with random equivalents",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v13

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |>
  filter(value != "0",
         number %in% to_keep) |>
  group_by(number, predicate_eng, source) |> 
  slice_sample(n = 1)  |> 
  distinct() |> 
  ungroup() |> 
  mutate(value = str_replace_all(value, "able, be", "able||| be"),
         value = str_replace_all(value, "bear, give", "bear||| give"),
         value = str_replace_all(value, ", shut", "||| shut"),
         value = str_replace_all(value, "cry, weep", "cry||| weep"),
         value = str_replace_all(value, "shoot, sting", "shoot||| sting"),
         value = str_replace_all(value, "stuck, get", "stuck||| get"),
         value = str_replace_all(value, ",", ";"),
         value = str_replace_all(value, "\\|\\|\\|", ","),
         value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1) |>  
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  select(ends_with("etym")) |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Verb etimology with random equivalents",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v14

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |>
  filter(value != "0",
         number %in% to_keep) |>
  group_by(number, predicate_eng, source) |> 
  slice_sample(n = 1)  |> 
  distinct() |> 
  ungroup() |> 
  mutate(value = str_replace_all(value, "able, be", "able||| be"),
         value = str_replace_all(value, "bear, give", "bear||| give"),
         value = str_replace_all(value, ", shut", "||| shut"),
         value = str_replace_all(value, "cry, weep", "cry||| weep"),
         value = str_replace_all(value, "shoot, sting", "shoot||| sting"),
         value = str_replace_all(value, "stuck, get", "stuck||| get"),
         value = str_replace_all(value, ",", ";"),
         value = str_replace_all(value, "\\|\\|\\|", ","),
         value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1) |>  
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  select(ends_with("etym")) |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Verb etimology with random equivalents",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

Только стимулы, для которых есть заполненная этимология

Если посмотреть только на те случаи, где заполнена этимология, то останется 21 пример.

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  na.omit() |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  filter(value != "0",
         number %in% to_keep) |> 
  distinct() |> 
  mutate(value = str_replace_all(value, "able, be", "able||| be"),
         value = str_replace_all(value, "bear, give", "bear||| give"),
         value = str_replace_all(value, ", shut", "||| shut"),
         value = str_replace_all(value, "cry, weep", "cry||| weep"),
         value = str_replace_all(value, "shoot, sting", "shoot||| sting"),
         value = str_replace_all(value, "stuck, get", "stuck||| get"),
         value = str_replace_all(value, ",", ";"),
         value = str_replace_all(value, "\\|\\|\\|", ","),
         value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1) |>  
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  select(ends_with("etym")) |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Verb etimology with common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

Глагольная этимология (old)

Все этимологии:

hclust

Code

et <- readxl::read_xlsx("../GM_merged_wide_verb_MK_0927.xlsx")
et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |>
  filter(value != "0") |> 
  distinct() |> 
  mutate(value = str_replace_all(value, "able, be", "able||| be"),
         value = str_replace_all(value, "bear, give", "bear||| give"),
         value = str_replace_all(value, ", shut", "||| shut"),
         value = str_replace_all(value, "cry, weep", "cry||| weep"),
         value = str_replace_all(value, "shoot, sting", "shoot||| sting"),
         value = str_replace_all(value, "stuck, get", "stuck||| get"),
         value = str_replace_all(value, ",", ";"),
         value = str_replace_all(value, "\\|\\|\\|", ","),
         value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1) |>  
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  select(ends_with("etym")) |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") ->
  etym

etym |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Verb etimology (all stimuli)",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

neighbornet

Code

etym  |> 
  neighborNet() |> 
  plot()
title(main = "Verb etimology (all stimuli)")

Кластеризация со случайным эквивалентом. Я запустил более 10 раз – ничего в структуре не поменялось, только длина ножек менялась.

hclust

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |> 
  filter(value != "0") |> 
  distinct() |> 
  mutate(value = str_replace_all(value, "able, be", "able||| be"),
         value = str_replace_all(value, "bear, give", "bear||| give"),
         value = str_replace_all(value, ", shut", "||| shut"),
         value = str_replace_all(value, "cry, weep", "cry||| weep"),
         value = str_replace_all(value, "shoot, sting", "shoot||| sting"),
         value = str_replace_all(value, "stuck, get", "stuck||| get"),
         value = str_replace_all(value, ",", ";"),
         value = str_replace_all(value, "\\|\\|\\|", ","),
         value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1) |> 
  group_by(number, source) |> 
  sample_n(size = 1) |> 
  ungroup() |> 
  mutate(value2 = 1) |>  
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  select(ends_with("etym")) |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Kono = Kono_etym,
         Mano = Mano_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") ->
  etym_random

etym_random |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Verb etimology with random equivalents",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

neighbornet

Code

etym_random  |> 
  neighborNet() |> 
  plot()
title(main = "Verb etimology with random equivalents")

Это сделано по следующей таблице:

Code

et |> 
  select(number, predicate_eng, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |> 
  filter(value != "0") |> 
  distinct() |> 
  mutate(value = str_replace_all(value, "able, be", "able||| be"),
         value = str_replace_all(value, "bear, give", "bear||| give"),
         value = str_replace_all(value, ", shut", "||| shut"),
         value = str_replace_all(value, "cry, weep", "cry||| weep"),
         value = str_replace_all(value, "shoot, sting", "shoot||| sting"),
         value = str_replace_all(value, "stuck, get", "stuck||| get"),
         value = str_replace_all(value, ",", ";"),
         value = str_replace_all(value, "\\|\\|\\|", ","),
         value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = value) |> 
  pivot_wider(names_from = source, values_from = value2, values_fill = "") |>
  arrange(number, predicate_eng, value) |> 
  select(-value)

number	predicate_eng	Guro_etym	Looma_etym	Mano_etym	Dan_etym	Bamana_etym	Kpelle_etym	Kono_etym
1	hurt				break (to)-2-wi
1	hurt				do-1-ke
1	hurt				go out (to)-bo
1	hurt		hurt-xona				hurt-xona	hurt-xona
1	hurt	illness-1-jankaro
1	hurt					pain-1-dimi
1	hurt			sufferance-4-waa
1	hurt				turn-pene
2	be sick with					catch-2-miita
2	be sick with				do-1-ke
3	be afraid of				fear-1-g’ila	fear-1-g’ila
3	be afraid of		fear-3-duwa	fear-3-duwa
3	be afraid of						fear-4-gaaxu	fear-4-gaaxu
4	throw	throw-1 (to)-fili	throw-1 (to)-fili			throw-1 (to)-fili	throw-1 (to)-fili	throw-1 (to)-fili
4	throw			throw-2 (to)-zu	throw-2 (to)-zu
5	be sufficient to				catch-3-catch
5	be sufficient to					go out (to)-bo
6	be like sb	go out (to)-bo		go out (to)-bo	go out (to)-bo	go out (to)-bo
6	be like sb		knock down-kula				knock down-kula	knock down-kula
7	have trust			confidence-dannaya
7	have trust				go out (to)-bo
7	have trust		lie down-1-d’a			lie down-1-d’a	lie down-1-d’a	lie down-1-d’a
7	have trust	send-2-bo
7	have trust			stand (to)-1-d’o			stand (to)-1-d’o
8	take	take-2-si		take-2-si	take-2-si
8	take					take-3-ta
8	take		take-5-sige				take-5-sige	take-5-sige
9	see what			look-2 (to)-ga
9	see what	see (to)-1-ye			see (to)-1-ye	see (to)-1-ye
9	see what		see (to)-3-ka				see (to)-3-ka	see (to)-3-ka
10	influence					press-1 (to)-digi
10	influence	put-2-kpa
11	meet			go out (to)-bo
11	meet					meet-ben
11	meet	see (to)-1-ye			see (to)-1-ye	see (to)-1-ye
11	meet		see (to)-3-ka
12	enter	send-2-bo
12	enter			throw-2 (to)-zu
12	enter						enter (to)-1	enter (to)-1
12	enter		enter (to)-1-so
12	enter					enter (to)-2-dzon
12	enter	enter (to)-3-wola		enter (to)-3-wola
12	enter				lie down-1-d’a
13	win				able, be-1-mo
13	win					beat-1-bugo
13	win				do-1-ke
13	win			prepare (to)-baa
13	win					receive-sodon
13	win						rise (to)-1-te
13	win				take-2-si
14	leave						take-5-sige
14	leave				go away (to)-go
14	leave	go out (to)-bo		go out (to)-bo		go out (to)-bo
14	leave		knock down-kula				knock down-kula	knock down-kula
15	chase		chase (to)-kpe			chase (to)-kpe
15	chase			come-3-nu
15	chase						go (to)-1-di	go (to)-1-di
15	chase	go away (to)-go
16	bend			bend-4-kula
16	bend					bend-1-bidin
16	bend		bend-3-kpilin	bend-3-kpilin			bend-3-kpilin	bend-3-kpilin
16	bend				bend-4-kula
17	say	say-1-fo			say-1-fo	say-1-fo
17	say			say-5-gee
17	say		say-6-yee				say-6-yee	say-6-yee
18	hold				catch-3-kun
18	hold		catch-4-song
18	hold					hold-mara
19	catch			catch-3-kun	catch-3-kun	catch-3-kun
19	catch		catch-4-song				catch-4-song	catch-4-song
19	catch	stand (to)-1-do
20	to milk			go out (to)-bo
20	to milk		knock down-kula				knock down-kula	knock down-kula
20	to milk					milk (to)-bidi
20	to milk	take-2-si
21	reach					arrive-1-k’e
21	reach						arrive-4-kite	arrive-4-kite
21	reach	go out (to)-bo		go out (to)-bo			go out (to)-bo
22	touch			0
22	touch		touch (to)-4-tungbong				touch (to)-4-tungbong
22	touch		0
22	touch						sit (to)-1-sigi
22	touch					touch (to)-1-maga
22	touch			touch (to)-2-pa	touch (to)-2-pa
22	touch						touch (to)-4-tungbong	touch (to)-4-tungbong
23	fight sb		do-4-goon	do-4-goon	do-4-goon		do-4-goon	do-4-goon
23	fight sb	measure (to)-2-dan
23	fight sb					war-1-kele
24	be friends with			do-1-ke
24	be friends with			lie down-1-d’a
25	think about sb			come-3-nu
25	think about sb				do-1-ke
25	think about sb					think (to)-1-miira
25	think about sb		walk-1-sigan				walk-1-sigan	walk-1-sigan
25	think about sb			walk-3-taga
26	eat		drink (to)-1-min				drink (to)-1-min	drink (to)-1-min
26	eat					eat (to)-1-don/domu
26	eat	eat (to)-2-bele		eat (to)-2-bele	eat (to)-2-bele
27	fry		fry (to)-geran	fry (to)-geran	fry (to)-geran	fry (to)-geran	fry (to)-geran	fry (to)-geran
28	wait			help-2-kpong
28	wait				remain-to
28	wait			stand (to)-1-d’o	stand (to)-1-d’o
28	wait					wait-1-kono
28	wait	wait-2-gben	wait-2-gben				wait-2-gben	wait-2-gben
29	forget			forget-2-nemu
29	forget			forget-1-nyina		forget-1-nyina
29	forget		forget-2-nemu				forget-2-nemu	forget-2-nemu
29	forget				go out (to)-bo
29	forget	loose (to)-1-sama
30	depend on	take-2-si			take-2-si
31	call					call-2-wele
31	call	call-3-dhee			call-3-dhee
31	call		call-5-toli				call-5-toli	call-5-toli
31	call			do-1-ke
32	meet, faire connaissance	know (to)-1-d’on		know (to)-1-d’on	know (to)-1-d’on	know (to)-1-d’on
32	meet, faire connaissance		know (to)-2-kolon				know (to)-2-kolon	know (to)-2-kolon
33	know someone	know (to)-1-d’on		know (to)-1-d’on	know (to)-1-d’on	know (to)-1-d’on
33	know someone		know (to)-2-kolon				know (to)-2-kolon	know (to)-2-kolon
34	play			blow-1-fe			blow-1-fe
34	play			kill-2-je
34	play						forge (to)-2-galin	forge (to)-2-galin
34	play	kill-2-je			kill-2-je
34	play					say-1-fo
34	play			strike-2-ma
35	avoid						go out (to)-bo
35	avoid		lie down-1-d’a				lie down-1-d’a
35	avoid	put-2-kpa
35	avoid			spread (to)-2-kpo	spread (to)-2-kpo
36	fabricate						0
36	fabricate	do-1-ke		do-1-ke	do-1-ke
36	fabricate		make-kpeteng				make-kpeteng
36	fabricate			prepare (to)-baa
36	fabricate					weave (to)-1-dan
37	mock				stand (to)-1-d’o
37	mock				tear (to)-pera
37	mock					go out (to)-bo
37	mock		knock down-kula
37	mock					laugh-1-jele	laugh-1-jele	laugh-1-jele
37	mock				stand (to)-1-d’o
39	seek			spread (to)-2-kpo
39	seek					search-1-nyini
39	seek	search-3-gini
39	seek						search-4-koli
39	seek		walk-1-sigan
39	seek			walk-3-taga
40	paint					draw (a picture)-nyege
40	paint	pass-2-gile
40	paint						rise (to)-1-te
40	paint				whiten-fele
40	paint			write-2-been
41	bite		bite (to)-kin			bite (to)-kin	bite (to)-kin	bite (to)-kin
41	bite				catch-3-kun
41	bite	stand (to)-1-d’o		stand (to)-1-d’o
42	be deprived	fall (to)-2-dia
42	be deprived			fail-5-kon
42	be deprived						fail-6-fenge	fail-6-fenge
42	be deprived			forget-1-nyina
42	be deprived	loose (to)-1-sama
42	be deprived					misfortune-1-bono
43	catch		chase (to)-kpe
43	catch					catch-2-miita
43	catch	catch-3-kun		catch-3-kun	catch-3-kun
43	catch		catch-4-song				catch-4-song	catch-4-song
44	break			break (to)-1-gali
44	break	break (to)-1-gali	break (to)-1-gali		break (to)-1-gali	break (to)-1-gali	break (to)-1-gali	break (to)-1-gali
44	break			break (to)-2-wi
45	flatter				do-1-ke
45	flatter			rise (to)-1-te			rise (to)-1-te	rise (to)-1-te
46	love			do-1-ke
46	love			go out (to)-bo
46	love			do-1-ke	do-1-ke
46	love	go out (to)-bo
46	love					love-xani
47	wave			go out (to)-bo
47	wave					shake-6-yuguyugu
48	dream						go out (to)-bo	go out (to)-bo
48	dream			kill-2-je
49	wash			wash-1-poli
49	wash	wash-2-zulu			wash-2-zulu
49	wash		wash-3-ko			wash-3-ko	wash-3-ko	wash-3-ko
50	put on	do-5-wo
50	put on		enter (to)-1-so				enter (to)-1-so
50	put on					enter (to)-2-dzon
50	put on				lie down-1-d’a
50	put on							pour-2 (to)-pu
51	call smth	call-3-dhee			call-3-dhee
51	call smth				do-1-ke
51	call smth			say-5-gee
51	call smth					speak-1-xo
52	punish		lie down-1-d’a
52	punish				lie down-3-wo
52	punish					punish-nyangi
52	punish	stand (to)-1-d’o
53	attack					lie down-1-d’a
53	attack					venom-baga
53	attack						break (to)-3-golo	break (to)-3-golo
53	attack		catch-4-song
53	attack					fall (to)-1-bele
53	attack				fall (to)-3-pele
53	attack					rush-3-gbidi
53	attack			sit (to)-2-yaga
53	attack	venom-baga
54	be filled with smth	fill (to)-1-pa	fill (to)-1-pa	fill (to)-1-pa	fill (to)-1-pa	fill (to)-1-pa	fill (to)-1-pa	fill (to)-1-pa
55	find smth	see (to)-1-ye			see (to)-1-ye	see (to)-1-ye
55	find smth		see (to)-3-ka				see (to)-3-ka	see (to)-3-ka
56	lack							go out (to)-bo
57	hate, detest	boil-3-fili
57	hate, detest				do-1-ke
57	hate, detest						end-6-kpe
57	hate, detest					hate-xoni
57	hate, detest		take-5-sige
58	like			lie down-1-d’a
58	like			catch-3-kun
58	like						catch-4-song
58	like				do-1-ke
58	like					good-2
58	like			good-2-di
58	like	please (to)-so
58	like		tasty-nene
59	need				catch-3-kun
60	surround					encircle-2-kooli
60	surround					encircle-1-minin
60	surround				encircle-2-kooli
60	surround	round-1-dhidhi		round-1-dhidhi
60	surround						round-3-tintan	round-3-tintan
60	surround		round-4-kanka
61	be left	end-2-bo
61	be left		leave (go away)-1-dho				leave (go away)-1-dho	leave (go away)-1-dho
61	be left			remain-to		remain-to
62	respond			go out (to)-bo
62	respond			round-1-dhidhi
62	respond						transform-ponden
62	respond			agree-3-son
62	respond					answer-1-jabi
62	respond				go out (to)-bo
62	respond		transform-ponden				transform-ponden
63	open			go out (to)-bo			go out (to)-bo	go out (to)-bo
63	open				open-1-polo
63	open					open-2-yele
63	open		stand (to)-1-d’o
64	be different				fall (to)-4-to
64	be different					go out (to)-bo
64	be different							knock down-kula
64	be different	take-2-si
65	fall behind	end-2-bo
65	fall behind						leave (go away)-1-dho	leave (go away)-1-dho
65	fall behind			remain-to		remain-to
66	plow				do-1-ke			do-1-ke
66	plow					farm-sene
66	plow			go out (to)-bo
66	plow						plant (to)-1-sing
66	plow	put-2-kpa
67	smell	stand (to)-1-d’o
68	traverse, cross			cut-1 (to)-kan	cut-1 (to)-kan
68	traverse, cross	cut-3 (to)-kini
68	traverse, cross					cut-4 (to)-tige
68	traverse, cross		cut-8 (to)-tebe				cut-8 (to)-tebe	cut-8 (to)-tebe
68	traverse, cross			spread (to)-2-kpo
69	sing		fall (to)-4-to	fall (to)-4-to	fall (to)-4-to		fall (to)-4-to	fall (to)-4-to
69	sing					lie down-1-d’a
69	sing	send-2-bo
70	write		mark-ponyang
70	write		do-1-ke
70	write	kill-2-je			kill-2-je
70	write			mark-ponyang			mark-ponyang	mark-ponyang
70	write					write-safe
71	drink	drink (to)-1-min		drink (to)-1-min	drink (to)-1-min	drink (to)-1-min
71	drink		drink (to)-2-kpole				drink (to)-2-kpole	drink (to)-2-kpole
72	melt						melt-2-senge
72	melt					melt-1-yeelen
72	melt			melt-2-senge	melt-2-senge			melt-2-senge
72	melt						pour-2 (to)-pu
73	approach				able, be-1-mo
73	approach	catch-4-song					catch-4-song
73	approach			go (to)-2-dho
73	approach					meet-ben
74	leave			go away (to)-go
74	leave				go away (to)-go
74	leave					go out (to)-bo
74	leave			rise (to)-2-wili
74	leave	take-2-si
74	leave		take-5-sige				take-5-sige	take-5-sige
75	75) cover	gather-1-dhen
75	75) cover	bend-2-bugun
75	75) cover					close-1 (to), shut (to)-tugu
75	75) cover	close-2 (to), shut (to)-tan
75	75) cover						close-3 (to), shut (to)-kporu
75	75) cover	fructify-ba
75	75) cover							lie down-1-d’a
75	75) cover			lie down-4-kpaa	lie down-4-kpaa
76	76) remember			come-3-nu
76	76) remember					descend-1-jigi
76	76) remember			lie down-1-d’a
76	76) remember	stand (to)-1-d’o
76	76) remember			take-2-si
76	76) remember			wake up-2-buo	wake up-2-buo
76	76) remember		walk-1-sigan					walk-1-sigan
77	77) help						go out (to)-bo
77	77) help			throw-2 (to)-zu
77	77) help				catch-3-kun
77	77) help	go out (to)-bo
77	77) help					help-1-deemaa
77	77) help		help-2-kpong	help-2-kpong			help-2-kpong
77	77) help							pass-3-tanbi
78	78) understand	hear (to)-moli	hear (to)-moli	hear (to)-moli	hear (to)-moli		hear (to)-moli	hear (to)-moli
78	78) understand					understand-fa’amu
79	79) fall into	descend-1-jigi				descend-1-jigi
79	79) fall into		fall (to)-4-to				fall (to)-4-to	fall (to)-4-to
79	79) fall into			lie down-1-d’a	lie down-1-d’a
80	80) cut with			wound-3-gii
80	80) cut with			cut-1 (to)-kan	cut-1 (to)-kan
80	80) cut with					cut-4 (to)-tige
80	80) cut with		cut-5 (to)-sege
80	80) cut with						cut-8 (to)-tebe	cut-8 (to)-tebe
80	80) cut with	wound-4-tugo
81	81) stick to smth				fasten-nodo	fasten-nodo
81	81) stick to smth		stuck, get (to)-kpatang				stuck, get (to)-kpatang	stuck, get (to)-kpatang
82	82) lose to smb			prepare (to)-baa
83	83) be happy about, se rejouir de				catch-3-kun
83	83) be happy about, se rejouir de				do-1-ke
83	83) be happy about, se rejouir de					good-2
83	83) be happy about, se rejouir de			good-2-di
83	83) be happy about, se rejouir de		tasty-nene				tasty-nene	tasty-nene
84	84) speak with						do-1-ke
84	84) speak with						fall (to)-4-to
84	84) speak with							fall (to)-4-to
84	84) speak with			go out (to)-bo			go out (to)-bo
84	84) speak with				kill-2-je
84	84) speak with			speak-3-we
84	84) speak with			stand (to)-1-d’o
84	84) speak with					talk-1-bado
85	85) give birth to					bear, give birth-1-banki
85	85) give birth to			save-2-dha
85	85) give birth to	bear, give birth-3-yaa		bear, give birth-3-yaa
85	85) give birth to					bear, give birth-5
85	85) give birth to						descend-1-jigi	descend-1-jigi
85	85) give birth to		receive-sodon
85	85) give birth to						see (to)-3-ka
85	85) give birth to				spread (to)-2-kpo
86	86) let fall smth					fall (to)-1-bele
86	86) let fall smth	fall (to)-2-dia
86	86) let fall smth		fall (to)-4-to				fall (to)-4-to	fall (to)-4-to
86	86) let fall smth					go out (to)-bo
86	86) let fall smth			lie down-1-d’a
87	87) diriger, gouverner			take-2-si
87	87) diriger, gouverner						go out (to)-bo
87	87) diriger, gouverner			say-5-gee
88	88) miss			catch-3-kun
88	88) miss						catch-4-song	catch-4-song
89	89) follow	go away (to)-go
89	89) follow				pass-2-gile
89	89) follow		put-1-bila				put-1-bila
90	90) climb down						put-1-bila
90	90) climb down	descend-1-jigi				descend-1-jigi	descend-1-jigi
90	90) climb down			descend-2-yolo	descend-2-yolo
90	90) climb down		knock down-kula
90	90) climb down							put-1-bila
91	91) listen to smth	hear (to)-moli				hear (to)-moli	hear (to)-moli
91	91) listen to smth				remain-to
91	91) listen to smth		stand (to)-1-d’o	stand (to)-1-d’o		stand (to)-1-d’o	stand (to)-1-d’o	stand (to)-1-d’o
92	92) listen to smb	go out (to)-bo
92	92) listen to smb		obey-kolo			obey-kolo
92	92) listen to smb				sit (to)-2-yaga
92	92) listen to smb			spread (to)-2-kpo
93	93) hear	hear (to)-moli	hear (to)-moli	hear (to)-moli	hear (to)-moli	hear (to)-moli	hear (to)-moli	hear (to)-moli
93	93) hear		stand (to)-1-d’o
94	94) mix up with smth					mix-4-nyaxami
94	94) mix up with smth		mix-6-sunpu				mix-6-sunpu
95	95) look at			end-3-nyia
95	95) look at					look-1 (to)-felen
95	95) look at			look-2 (to)-ga	look-2 (to)-ga
95	95) look at						look-4 (to)-kpele
95	95) look at	search-3-gini
95	95) look at						see (to)-3-ka	see (to)-3-ka
96	96) take off			go out (to)-bo	go out (to)-bo	go out (to)-bo		go out (to)-bo
96	96) take off		knock down-kula				knock down-kula
97	97) appear in a dream					dream-1-k’uyi
97	97) appear in a dream				go out (to)-bo		go out (to)-bo	go out (to)-bo
97	97) appear in a dream			kill-2-je
98	98) agree			lie down-1-d’a
98	98) agree			0
98	98) agree					agree-3-son
98	98) agree		lie down-1-d’a
98	98) agree				speak-3-we
98	98) agree			stand (to)-1-d’o
98	98) agree						tasty-nene
99	99) dispute			do-1-ke
99	99) dispute						do-1-ke	do-1-ke
99	99) dispute		noise-1-sonxo
99	99) dispute			stand (to)-1-d’o
99	99) dispute					war-1-kele
101	101) shoot at					destroy (to)-1-te
101	101) shoot at			lie down-1-d’a	lie down-1-d’a
101	101) shoot at					shoot, sting (to)-bon
101	101) shoot at		stand (to)-1-to				stand (to)-1-to
101	101) shoot at	throw-1 (to)-fili
102	102) pour (dry product)				do-1-ke	do-1-ke
102	102) pour (dry product)							go out (to)-bo
102	102) pour (dry product)		pour-2 (to)-pu				pour-2 (to)-pu
103	103) lose	loose (to)-1-sama
103	103) lose					drop-1 (to)-budun
103	103) lose	fall (to)-2-dia
103	103) lose		fall (to)-4-to
103	103) lose			forget-1-nyina
103	103) lose				loose (to)-2-leenu			loose (to)-2-leenu
103	103) lose						throw-1 (to)-fili
104	104) drown				remain-to
104	104) drown				die-1-kha
104	104) drown					disappear-tunu
104	104) drown		enter (to)-1-so
104	104) drown						leave (go away)-1-dho	leave (go away)-1-dho
104	104) drown			remain-to
105	105) kill		kill-1-faxa			kill-1-faxa	kill-1-faxa	kill-1-faxa
105	105) kill	kill-2-je		kill-2-je	kill-2-je
106	106) hit						0hit-kele	0hit-kele
106	106) hit					beat-1-bugo
106	106) hit		beat-2-dokpe
106	106) hit			strike-2-ma	strike-2-ma
107	107) kiss							stand (to)-1-d’o
108	108) read			read-2-lonong
108	108) read							go out (to)-bo
108	108) read		read-1-kara			read-1-kara
108	108) read	say-1-fo			say-1-fo
108	108) read			say-5-gee
109	109) move smth						0move-tumu	0move-tumu
109	109) move smth			go out (to)-bo
109	109) move smth					move (to)-3-lamaga
109	109) move smth				noise-2-vin
109	109) move smth			shake-1-miimii
110	110) respect			do-1-ke
110	110) respect		give (to)-3-fe
110	110) respect	go out (to)-bo
110	110) respect						heavy-1-bi	heavy-1-bi
110	110) respect					respect-3-bonya
110	110) respect			sit (to)-2-yaga
111	111) disdain			spoil-1-sile
111	111) disdain				do-1-ke
111	111) disdain	go out (to)-bo		go out (to)-bo
111	111) disdain			lie down-1-d’a
111	111) disdain					loathe-nyigin
111	111) disdain				see (to)-1-ye
112	112) be happy about					fill (to)-1-pa
112	112) be happy about			good-2-di
112	112) be happy about					satisfy-wasa
112	112) be happy about		tasty-nene				tasty-nene	tasty-nene
113	113) fall in love with			lie down-1-d’a	lie down-1-d’a
113	113) fall in love with		love-xani
113	113) fall in love with					passion-jarabi
113	113) fall in love with						stand (to)-1-to	stand (to)-1-to
114	114) trust in sb					lie down-1-d’a		lie down-1-d’a
115	115) have pity of		catch-4-song
115	115) have pity of			do-1-ke	do-1-ke
115	115) have pity of					pity-2-hina
116	116) be envious of			cut-1 (to)-kan
116	116) be envious of				do-1-ke
116	116) be envious of	do-5-wo
116	116) be envious of						fill (to)-1-pa
116	116) be envious of					hate-xoni
116	116) be envious of			swell-3-fuunu
117	117) be angry with			bind-gidi
117	117) be angry with			tie-1
117	117) be angry with			bend-4-kula
117	117) be angry with		bitter-2-xunan
117	117) be angry with	boil-3-fili
117	117) be angry with							catch-4-song
117	117) be angry with					pain-1-dimi
117	117) be angry with						pain-2-soli
117	117) be angry with				take-2-si
118	118) be surprised about				do-1-ke
118	118) be surprised about							knock down-kula
118	118) be surprised about						throw-1 (to)-fili
118	118) be surprised about			throw-2 (to)-zu
119	119) love smth				do-1-ke
119	119) love smth					good-2-di
119	119) love smth	please (to)-so
119	119) love smth		tasty-nene
120	120) enjoy/ take pleasure in					go out (to)-bo
120	120) enjoy/ take pleasure in		knock down-kula
120	120) enjoy/ take pleasure in				see (to)-1-ye
120	120) enjoy/ take pleasure in			take-2-si
120	120) enjoy/ take pleasure in						tasty-nene
121	121) want				do-1-ke
122	122) be angry with = 117?	boil-3-fili
122	122) be angry with = 117?							catch-4-song
123	123) take offense by sb						do-1-ke
123	123) take offense by sb							go out (to)-bo
123	123) take offense by sb				lie down-1-d’a
124	124) make sad sb							chase (to)-kpe
124	124) make sad sb		cut-8 (to)-tebe
124	124) make sad sb			do-1-ke
124	124) make sad sb					enter (to)-2-dzon
124	124) make sad sb						kill-1-faxa
124	124) make sad sb		knock down-kula
124	124) make sad sb				wound-3-gii
125	125) be surprised by					end-1-ban
125	125) be surprised by							throw-1 (to)-fili
126	126) despise sb			kill-2-je
126	126) despise sb					better-fisa
126	126) despise sb				do-1-ke
126	126) despise sb						go out (to)-bo	go out (to)-bo
126	126) despise sb			stand (to)-1-d’o
127	127) be sad because of sb			fill (to)-1-pa
127	127) be sad because of sb			break (to)-1-gali
127	127) be sad because of sb					cry, weep (to)-1-kasi
127	127) be sad because of sb		cut-8 (to)-tebe
127	127) be sad because of sb	stand (to)-1-d’o
127	127) be sad because of sb							tasty-nene
128	128) be annoyed by sb	boil-3-fili
128	128) be annoyed by sb							catch-4-song
128	128) be annoyed by sb					pain-1-dimi
129	129) sympathise to sb						help-2-kpong
129	129) sympathise to sb						go out (to)-bo	go out (to)-bo
130	130) be embarrassed by sb		cold-3-deli
130	130) be embarrassed by sb			embarrass-2-kpala	embarrass-2-kpala		embarrass-2-kpala
130	130) be embarrassed by sb					hinder-1-degun

Адложная этимология v2

Все примеры, без фильтрации

Code

et <- readxl::read_xlsx("../GM_merged_wide_adpositions_MK_0926.xlsx")
et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |> 
  filter(value != "0") |> 
  distinct() |> 
  mutate(value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1,
         source = str_replace(source, " ", "_")) |> 
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  select(ends_with("etym"))  |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Adposition etimology (all stimuli)",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

Только стимулы, для которых есть все языки

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |> 
  filter(value != "0",
         number %in% to_keep) |> 
  distinct() |> 
  mutate(value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1,
         source = str_replace(source, " ", "_")) |> 
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  select(ends_with("etym"))  |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Adposition etimology, common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

Только стимулы, для которых есть все языки, случайная этимология для каждого языка

v1

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |> 
  filter(value != "0",
         number %in% to_keep) |> 
  distinct() |> 
  mutate(value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1,
         source = str_replace(source, " ", "_")) |> 
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  group_by(number) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(ends_with("etym"))  |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Adposition etimology, common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v2

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |> 
  filter(value != "0",
         number %in% to_keep) |> 
  distinct() |> 
  mutate(value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1,
         source = str_replace(source, " ", "_")) |> 
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  group_by(number) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(ends_with("etym"))  |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Adposition etimology, common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v3

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |> 
  filter(value != "0",
         number %in% to_keep) |> 
  distinct() |> 
  mutate(value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1,
         source = str_replace(source, " ", "_")) |> 
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  group_by(number) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(ends_with("etym"))  |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Adposition etimology, common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v4

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |> 
  filter(value != "0",
         number %in% to_keep) |> 
  distinct() |> 
  mutate(value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1,
         source = str_replace(source, " ", "_")) |> 
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  group_by(number) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(ends_with("etym"))  |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Adposition etimology, common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v5

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |> 
  filter(value != "0",
         number %in% to_keep) |> 
  distinct() |> 
  mutate(value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1,
         source = str_replace(source, " ", "_")) |> 
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  group_by(number) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(ends_with("etym"))  |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Adposition etimology, common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v6

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |> 
  filter(value != "0",
         number %in% to_keep) |> 
  distinct() |> 
  mutate(value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1,
         source = str_replace(source, " ", "_")) |> 
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  group_by(number) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(ends_with("etym"))  |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Adposition etimology, common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v7

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |> 
  filter(value != "0",
         number %in% to_keep) |> 
  distinct() |> 
  mutate(value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1,
         source = str_replace(source, " ", "_")) |> 
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  group_by(number) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(ends_with("etym"))  |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Adposition etimology, common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v8

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |> 
  filter(value != "0",
         number %in% to_keep) |> 
  distinct() |> 
  mutate(value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1,
         source = str_replace(source, " ", "_")) |> 
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  group_by(number) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(ends_with("etym"))  |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Adposition etimology, common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v9

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |> 
  filter(value != "0",
         number %in% to_keep) |> 
  distinct() |> 
  mutate(value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1,
         source = str_replace(source, " ", "_")) |> 
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  group_by(number) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(ends_with("etym"))  |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Adposition etimology, common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v10

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |> 
  filter(value != "0",
         number %in% to_keep) |> 
  distinct() |> 
  mutate(value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1,
         source = str_replace(source, " ", "_")) |> 
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  group_by(number) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(ends_with("etym"))  |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Adposition etimology, common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v11

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |> 
  filter(value != "0",
         number %in% to_keep) |> 
  distinct() |> 
  mutate(value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1,
         source = str_replace(source, " ", "_")) |> 
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  group_by(number) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(ends_with("etym"))  |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Adposition etimology, common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v12

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |> 
  filter(value != "0",
         number %in% to_keep) |> 
  distinct() |> 
  mutate(value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1,
         source = str_replace(source, " ", "_")) |> 
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  group_by(number) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(ends_with("etym"))  |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Adposition etimology, common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v13

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |> 
  filter(value != "0",
         number %in% to_keep) |> 
  distinct() |> 
  mutate(value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1,
         source = str_replace(source, " ", "_")) |> 
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  group_by(number) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(ends_with("etym"))  |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Adposition etimology, common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

v14

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |> 
  filter(value != "0",
         number %in% to_keep) |> 
  distinct() |> 
  mutate(value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1,
         source = str_replace(source, " ", "_")) |> 
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  group_by(number) |> 
  slice_sample(n = 1) |> 
  ungroup() |> 
  select(ends_with("etym"))  |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Adposition etimology, common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

Адложная этимология (old)

Все этимологии:

hclust

Code

et <- readxl::read_xlsx("../GM_merged_wide_adpositions_MK_0926.xlsx")
et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |> 
  filter(value != "0") |> 
  distinct() |> 
  mutate(value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1,
         source = str_replace(source, " ", "_")) |> 
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  select(ends_with("etym"))  |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Mano = Mano_etym,
         Kono = Kono_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") ->
  etym

etym |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Adposition etimology (all stimuli)",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

neighbornet

Code

etym  |> 
  neighborNet() |> 
  plot()
title(main = "Adposition etimology (all stimuli)")

Кластеризация со случайным эквивалентом. Я запустил более 10 раз – ничего в структуре не поменялось, только длина ножек менялась.

hclust

Code

et |> 
  select(number, predicate_eng, stimuli, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |> 
  filter(value != "0") |> 
  distinct() |> 
  mutate(value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = 1) |> 
  group_by(number, source) |> 
  sample_n(size = 1) |> 
  ungroup() |> 
  mutate(value2 = 1,
         source = str_replace(source, " ", "_")) |>  
  pivot_wider(names_from = source, values_from = value2, values_fill = 0) |> 
  select(ends_with("etym")) |> 
  rename(Guro = Guro_etym,
         Looma = Looma_etym,
         Kono = Kono_etym,
         Mano = Mano_etym,
         Dan_Gweetaa = Dan_etym,
         Bamana = Bamana_etym,
         Kpelle = Kpelle_etym) |> 
  t() |> 
  dist(method = "binary") ->
  etym_random

etym_random |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Adposition etimology with random equivalents",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

neighbornet

Code

etym_random  |> 
  neighborNet() |> 
  plot()
title(main = "Adposition etimology with random equivalents")

Это сделано по следующей таблице:

Code

et |> 
  select(number, predicate_eng, ends_with("etym")) |> 
  pivot_longer(names_to = "source", 
               values_to = "value", 
               ends_with("etym")) |> 
  na.omit() |> 
  filter(value != "0") |> 
  distinct() |> 
  mutate(value = str_replace_all(value, "able, be", "able||| be"),
         value = str_replace_all(value, "bear, give", "bear||| give"),
         value = str_replace_all(value, ", shut", "||| shut"),
         value = str_replace_all(value, "cry, weep", "cry||| weep"),
         value = str_replace_all(value, "shoot, sting", "shoot||| sting"),
         value = str_replace_all(value, "stuck, get", "stuck||| get"),
         value = str_replace_all(value, ",", ";"),
         value = str_replace_all(value, "\\|\\|\\|", ","),
         value = str_split(value, ";")) |> 
  unnest_longer(value) |> 
  distinct() |> 
  mutate(value2 = value) |> 
  pivot_wider(names_from = source, values_from = value2) |>
  arrange(number, predicate_eng, value) |> 
  select(-value)

number	predicate_eng	Dan etym	Guro etym	Looma etym	Mano etym	Kono etym	Kpelle etym	Bamana etym
1	hurt	postposition with-3-ga	NA	NA	NA	NA	NA	NA
1	hurt	postposition-on-1-ma	NA	NA	NA	NA	NA	NA
2	be sick with	NA	NA	NA	NA	NA	NA	postposition at-la
2	be sick with	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	NA
3	be afraid of	NA	NA	NA	postposition with-3-ga	NA	NA	NA
3	be afraid of	NA	NA	NA	NA	NA	NA	eye-nya
3	be afraid of	NA	palm of hand-degere	NA	NA	NA	NA	NA
3	be afraid of	NA	NA	postposition by-ba	NA	NA	NA	NA
3	be afraid of	postposition-for-1-gon	NA	NA	NA	NA	NA	NA
3	be afraid of	NA	NA	NA	postposition-for-2-len	NA	NA	NA
3	be afraid of	NA	NA	NA	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	NA
5	be sufficient to	NA	NA	NA	NA	arm-2-je	arm-2-je	NA
5	be sufficient to	NA	NA	NA	NA	hand	hand	NA
6	be like sb	NA	NA	NA	postposition by-ba	NA	NA	NA
6	be like sb	NA	NA	postposition with-3-ga	NA	NA	NA	NA
6	be like sb	postposition-on-1-ma	postposition-on-1-ma	NA	NA	postposition-on-1-ma	postposition-on-1-ma	NA
6	be like sb	NA	NA	NA	NA	NA	NA	side-3-fe
7	have trust	before-dhie	NA	NA	NA	NA	NA	NA
7	have trust	NA	NA	NA	NA	NA	NA	postposition at-la
7	have trust	NA	postposition-for-2-len	NA	NA	NA	NA	NA
7	have trust	NA	NA	NA	postposition-on-1-ma	NA	postposition-on-1-ma	NA
7	have trust	NA	NA	NA	surface-1-ta	surface-1-ta	surface-1-ta	NA
9	see what	postposition-on-1-ma	NA	NA	NA	NA	NA	NA
10	influence	NA	surface-1-ta	NA	NA	NA	NA	NA
11	meet	NA	postposition with-2-yan	NA	NA	NA	NA	NA
11	meet	NA	NA	NA	NA	postposition with-3-ga	postposition with-3-ga	NA
11	meet	postposition-on-1-ma	NA	NA	postposition-on-1-ma	NA	NA	NA
13	win	NA	postposition-for-2-len	NA	NA	NA	NA	NA
13	win	postposition-on-1-ma	NA	NA	postposition-on-1-ma	NA	postposition-on-1-ma	NA
13	win	NA	NA	NA	NA	NA	NA	postposition-on-2-xan
14	leave	NA	NA	NA	NA	NA	NA	belly-2-xono
14	leave	NA	NA	NA	NA	NA	NA	stomach
15	chase	NA	postposition with-2-yan	NA	NA	NA	NA	NA
15	chase	NA	NA	NA	postposition with-3-ga	postposition with-3-ga	postposition with-3-ga	NA
17	say	NA	NA	NA	NA	NA	NA	postposition with-2-yan
17	say	postposition-for-2-len	postposition-for-2-len	NA	postposition-for-2-len	NA	NA	NA
17	say	NA	NA	postposition-on-1-ma	NA	postposition-on-1-ma	postposition-on-1-ma	NA
18	hold	NA	NA	NA	NA	arm-2-je	arm-2-je	NA
18	hold	NA	NA	NA	NA	hand	hand	NA
18	hold	NA	NA	NA	postposition-on-1-ma	NA	NA	NA
18	hold	NA	surface-1-ta	NA	NA	NA	NA	NA
19	catch	NA	NA	NA	NA	NA	NA	postposition at-la
19	catch	NA	postposition-on-1-ma	NA	NA	NA	NA	NA
21	reach	NA	NA	NA	NA	NA	NA	postposition at-la
21	reach	NA	postposition-for-2-len	NA	NA	NA	NA	NA
21	reach	NA	NA	NA	NA	postposition-on-1-ma	postposition-on-1-ma	NA
21	reach	NA	NA	NA	surface-1-ta	NA	NA	NA
22	touch	NA	NA	NA	NA	NA	NA	postposition at-la
22	touch	NA	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	NA
23	fight sb	NA	postposition by-ba	NA	NA	NA	NA	NA
23	fight sb	postposition with-3-ga	NA	postposition with-3-ga	NA	NA	NA	NA
24	be friends with	NA	NA	NA	NA	NA	back-1-pulu	NA
24	be friends with	NA	postposition with-2-yan	NA	NA	NA	NA	NA
24	be friends with	NA	NA	postposition with-3-ga	NA	postposition with-3-ga	postposition with-3-ga	NA
24	be friends with	NA	NA	NA	side-3-fe	NA	NA	NA
25	think about sb	NA	NA	NA	NA	NA	NA	postposition at-la
25	think about sb	postposition in-3-gi	NA	NA	NA	NA	NA	NA
25	think about sb	NA	postposition-for-2-len	NA	NA	NA	NA	NA
25	think about sb	NA	NA	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	NA
25	think about sb	NA	NA	NA	surface-1-ta	NA	NA	NA
28	wait	postposition-for-1-gon	NA	NA	NA	NA	NA	NA
29	forget	NA	NA	NA	NA	NA	NA	back-3-xo
29	forget	postposition with-3-ga	NA	NA	NA	NA	NA	NA
29	forget	NA	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	NA
30	depend on	NA	NA	NA	NA	NA	NA	postposition at-la
30	depend on	NA	postposition by-ba	NA	NA	NA	NA	NA
30	depend on	postposition-on-1-ma	NA	NA	NA	NA	NA	NA
35	avoid	postposition with-3-ga	NA	NA	NA	NA	NA	NA
35	avoid	NA	postposition-on-1-ma	NA	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	NA
37	mock	NA	NA	postposition in-2-sun	NA	NA	NA	NA
37	mock	postposition-on-1-ma	NA	NA	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	NA
38	have	NA	NA	NA	NA	arm-2-je	arm-2-je	NA
38	have	NA	NA	NA	NA	hand	hand	NA
38	have	NA	palm of hand-degere	NA	NA	NA	NA	NA
38	have	postposition-for-1-gon	NA	NA	NA	NA	NA	NA
38	have	NA	NA	NA	NA	NA	NA	side-3-fe
39	seek	NA	NA	NA	postposition with-3-ga	NA	NA	NA
40	paint	NA	postposition-on-1-ma	NA	NA	NA	postposition-on-1-ma	NA
41	bite	NA	postposition-on-1-ma	NA	NA	NA	NA	NA
42	be deprived	NA	NA	NA	postposition with-3-ga	NA	NA	NA
42	be deprived	NA	NA	NA	NA	NA	NA	postposition at-la
42	be deprived	NA	NA	NA	NA	postposition in-2-sun	postposition in-2-sun	NA
42	be deprived	NA	NA	NA	postposition in-3-gi	NA	NA	NA
42	be deprived	NA	NA	postposition-on-1-ma	NA	NA	NA	NA
46	love	NA	NA	NA	postposition with-3-ga	NA	NA	NA
46	love	NA	NA	postposition-for-3-be	NA	NA	NA	NA
46	love	NA	postposition-on-1-ma	NA	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	NA
46	love	NA	NA	NA	NA	NA	NA	side-3-fe
48	dream	NA	NA	NA	postposition with-3-ga	postposition with-3-ga	postposition with-3-ga	NA
50	put on	NA	postposition-on-1-ma	NA	NA	NA	NA	NA
51	call smth	NA	NA	postposition with-3-ga	NA	postposition with-3-ga	postposition with-3-ga	NA
51	call smth	NA	NA	NA	postposition-for-2-len	NA	NA	NA
52	punish	NA	postposition in-3-gi	NA	NA	NA	NA	NA
52	punish	NA	NA	postposition-on-1-ma	NA	NA	NA	NA
52	punish	surface-1-ta	NA	NA	NA	NA	NA	NA
53	attack	NA	NA	NA	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma
53	attack	NA	NA	NA	NA	NA	NA	postposition-on-2-xan
53	attack	surface-1-ta	surface-1-ta	NA	NA	NA	NA	NA
54	be filled with smth	NA	NA	NA	NA	NA	NA	postposition at-la
54	be filled with smth	NA	postposition with-2-yan	NA	NA	NA	NA	NA
54	be filled with smth	postposition with-3-ga	NA	postposition with-3-ga	postposition with-3-ga	postposition with-3-ga	postposition with-3-ga	NA
55	find smth	postposition-on-1-ma	NA	NA	NA	NA	NA	NA
56	lack	NA	NA	NA	0	NA	NA	NA
56	lack	NA	NA	NA	postposition with-3-ga	NA	NA	NA
57	hate, detest	NA	NA	NA	postposition in-3-gi	NA	NA	NA
57	hate, detest	postposition-for-1-gon	NA	NA	NA	NA	NA	NA
57	hate, detest	NA	postposition-on-1-ma	NA	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	NA
58	like	NA	NA	NA	side-3-fe	NA	NA	NA
58	like	NA	NA	NA	0	NA	NA	NA
58	like	NA	NA	NA	NA	NA	NA	postposition with-2-yan
58	like	NA	postposition-for-2-len	NA	postposition-for-2-len	NA	NA	NA
58	like	NA	NA	postposition-for-3-be	NA	NA	NA	NA
58	like	NA	NA	NA	NA	postposition-on-1-ma	NA	NA
59	need	NA	NA	NA	NA	NA	NA	postposition at-la
59	need	NA	NA	postposition by-ba	NA	NA	NA	NA
59	need	postposition-on-1-ma	NA	NA	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	NA
60	surround	around-zi	around-zi	NA	NA	NA	NA	NA
60	surround	NA	NA	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	NA	NA
61	be left	NA	NA	NA	NA	arm-2-je	arm-2-je	NA
61	be left	NA	NA	NA	NA	hand	hand	NA
61	be left	NA	palm of hand-degere	NA	NA	NA	NA	NA
62	respond	NA	NA	NA	NA	postposition-on-1-ma	NA	NA
64	be different	NA	NA	NA	NA	NA	NA	postposition at-la
64	be different	postposition-on-1-ma	postposition-on-1-ma	NA	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	NA
65	fall behind	NA	NA	back-1-pulu	NA	back-1-pulu	back-1-pulu	NA
65	fall behind	NA	NA	NA	NA	NA	NA	back-3-xo
65	fall behind	NA	bottom-1-ju	NA	NA	NA	NA	NA
65	fall behind	NA	NA	NA	side-3-fe	NA	NA	NA
67	smell	NA	postposition with-2-yan	NA	NA	NA	NA	NA
67	smell	NA	NA	postposition-for-3-be	NA	NA	NA	NA
67	smell	postposition-on-1-ma	NA	NA	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	NA
67	smell	NA	NA	NA	NA	NA	NA	trace-1-pon
68	traverse, cross	NA	NA	NA	surface-1-ta	NA	NA	NA
73	approach	NA	around-zi	NA	NA	NA	NA	NA
73	approach	postposition-on-1-ma	NA	NA	NA	NA	NA	postposition-on-1-ma
74	leave	NA	NA	NA	NA	NA	NA	postposition at-la
75	75) cover	surface-1-ta	surface-1-ta	NA	NA	NA	NA	NA
75	75) cover	postposition-on-1-ma	postposition-on-1-ma	NA	NA	NA	NA	NA
75	75) cover	NA	surface-1-ta	NA	surface-1-ta	NA	NA	NA
76	76) remember	NA	NA	postposition in-2-sun	NA	NA	NA	NA
76	76) remember	NA	NA	NA	NA	NA	NA	postposition at-la
76	76) remember	NA	postposition in-3-gi	NA	NA	NA	NA	NA
76	76) remember	postposition with-3-ga	NA	NA	postposition with-3-ga	NA	NA	NA
76	76) remember	NA	NA	postposition-on-1-ma	NA	postposition-on-1-ma	NA	NA
76	76) remember	NA	NA	NA	surface-1-ta	NA	NA	NA
76	76) remember	NA	NA	NA	NA	NA	surface-2-ga	NA
77	77) help	NA	postposition by-ba	postposition by-ba	NA	NA	NA	NA
77	77) help	NA	NA	NA	postposition-on-1-ma	NA	postposition-on-1-ma	NA
77	77) help	NA	NA	NA	NA	trace-1-pon	NA	NA
79	79) fall into	NA	NA	NA	NA	NA	NA	postposition-on-2-xan
80	80) cut with	NA	postposition with-2-yan	NA	NA	NA	NA	NA
80	80) cut with	postposition with-3-ga	NA	postposition with-3-ga	postposition with-3-ga	postposition with-3-ga	postposition with-3-ga	NA
81	81) stick to smth	NA	NA	postposition by-ba	NA	NA	NA	NA
81	81) stick to smth	postposition-on-1-ma	NA	NA	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	NA
81	81) stick to smth	NA	surface-1-ta	NA	NA	NA	NA	NA
82	82) lose to smb	NA	NA	NA	NA	NA	NA	arm-1-bolo
82	82) lose to smb	NA	NA	NA	NA	NA	NA	hand
82	82) lose to smb	NA	NA	NA	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	NA
83	83) be happy about, se rejouir de	NA	postposition by-ba	NA	NA	NA	NA	NA
83	83) be happy about, se rejouir de	NA	NA	NA	NA	NA	NA	postposition at-la
83	83) be happy about, se rejouir de	NA	postposition-on-1-ma	postposition-on-1-ma	NA	postposition-on-1-ma	postposition-on-1-ma	NA
84	84) speak with	NA	NA	NA	side-3-fe	NA	NA	NA
84	84) speak with	postposition-for-2-len	NA	NA	postposition-for-2-len	NA	NA	NA
84	84) speak with	NA	NA	NA	NA	NA	NA	side-3-fe
85	85) give birth to	NA	NA	NA	postposition with-3-ga	postposition with-3-ga	postposition with-3-ga	NA
86	86) let fall smth	NA	NA	NA	NA	NA	NA	arm-1-bolo
86	86) let fall smth	NA	NA	NA	NA	NA	NA	hand
87	87) diriger, gouverner	before-dhie	NA	NA	NA	NA	NA	NA
87	87) diriger, gouverner	NA	NA	NA	NA	NA	NA	postposition with-2-yan
88	88) miss	postposition with-3-ga	NA	NA	NA	NA	NA	NA
88	88) miss	NA	NA	NA	NA	NA	NA	postposition at-la
88	88) miss	surface-1-ta	NA	NA	NA	NA	NA	NA
89	89) follow	NA	NA	back-1-pulu	NA	NA	back-1-pulu	NA
89	89) follow	NA	bottom-1-ju	NA	NA	NA	NA	NA
89	89) follow	occiput-3-kee	NA	NA	NA	NA	NA	NA
89	89) follow	NA	NA	NA	NA	NA	NA	postposition at-la
90	90) climb down	NA	NA	NA	NA	NA	NA	postposition at-la
91	91) listen to smth	postposition-on-1-ma	NA	postposition-on-1-ma	postposition-on-1-ma	NA	postposition-on-1-ma	NA
91	91) listen to smth	NA	NA	NA	NA	NA	NA	side-3-fe
92	92) listen to smb	NA	NA	NA	NA	NA	NA	postposition with-2-yan
92	92) listen to smb	NA	NA	postposition-for-3-be	NA	NA	NA	NA
93	93) hear	NA	NA	postposition-on-1-ma	NA	NA	NA	NA
94	94) mix up with smth	NA	postposition with-2-yan	NA	NA	NA	NA	NA
94	94) mix up with smth	postposition with-3-ga	NA	NA	postposition with-3-ga	postposition with-3-ga	postposition with-3-ga	NA
97	97) appear in a dream	NA	NA	NA	NA	NA	NA	postposition at-la
97	97) appear in a dream	postposition with-3-ga	NA	NA	postposition with-3-ga	postposition with-3-ga	postposition with-3-ga	NA
98	98) agree	NA	NA	NA	postposition by-ba	NA	NA	NA
98	98) agree	NA	NA	NA	NA	postposition in-2-sun	NA	NA
98	98) agree	postposition-on-1-ma	NA	postposition-on-1-ma	NA	NA	NA	postposition-on-1-ma
99	99) dispute	NA	postposition by-ba	NA	NA	NA	NA	NA
99	99) dispute	NA	postposition with-2-yan	NA	NA	NA	NA	NA
99	99) dispute	postposition with-3-ga	NA	NA	NA	NA	NA	NA
99	99) dispute	NA	NA	NA	postposition-on-1-ma	NA	NA	NA
100	100) cost	NA	NA	NA	NA	NA	NA	postposition with-2-yan
100	100) cost	NA	NA	postposition with-3-ga	postposition with-3-ga	postposition with-3-ga	postposition with-3-ga	NA
101	101) shoot at	NA	postposition by-ba	NA	NA	NA	NA	NA
101	101) shoot at	postposition-on-1-ma	NA	postposition-on-1-ma	postposition-on-1-ma	NA	postposition-on-1-ma	NA
103	103) lose	NA	NA	NA	NA	NA	NA	back-3-xo
104	104) drown	NA	NA	bottom-2-bu	NA	NA	NA	NA
104	104) drown	NA	NA	NA	NA	NA	NA	postposition at-la
104	104) drown	postposition by-ba	NA	NA	postposition by-ba	NA	NA	NA
104	104) drown	NA	NA	NA	NA	postposition in-2-sun	postposition in-2-sun	NA
107	107) kiss	NA	postposition-on-1-ma	NA	NA	NA	NA	NA
110	110) respect	postposition in-3-gi	NA	NA	NA	NA	NA	NA
110	110) respect	NA	NA	postposition-on-1-ma	NA	NA	NA	NA
111	111) disdain	NA	NA	NA	NA	NA	NA	postposition at-la
111	111) disdain	postposition in-3-gi	NA	NA	NA	NA	NA	NA
111	111) disdain	NA	postposition-on-1-ma	NA	postposition-on-1-ma	postposition-on-1-ma	NA	NA
111	111) disdain	NA	NA	NA	side-3-fe	NA	NA	NA
111	111) disdain	NA	NA	NA	NA	NA	trace-1-pon	NA
112	112) be happy about	NA	NA	NA	0	NA	NA	NA
112	112) be happy about	NA	NA	NA	NA	NA	NA	postposition at-la
112	112) be happy about	NA	NA	NA	NA	NA	NA	postposition with-2-yan
112	112) be happy about	NA	NA	postposition-on-1-ma	NA	NA	NA	NA
112	112) be happy about	NA	NA	NA	side-3-fe	NA	NA	NA
112	112) be happy about	NA	NA	NA	NA	trace-1-pon	trace-1-pon	NA
113	113) fall in love with	NA	NA	NA	NA	NA	NA	postposition at-la
113	113) fall in love with	postposition in-3-gi	NA	NA	NA	NA	NA	NA
113	113) fall in love with	NA	NA	postposition with-3-ga	NA	NA	NA	NA
113	113) fall in love with	NA	NA	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	NA
114	114) trust in sb	NA	NA	NA	NA	NA	NA	postposition at-la
114	114) trust in sb	NA	surface-1-ta	NA	NA	surface-1-ta	NA	NA
115	115) have pity of	NA	NA	NA	NA	NA	NA	postposition at-la
115	115) have pity of	NA	NA	NA	postposition-on-1-ma	NA	postposition-on-1-ma	NA
116	116) be envious of	postposition with-3-ga	NA	NA	NA	NA	NA	NA
116	116) be envious of	NA	postposition-on-1-ma	NA	postposition-on-1-ma	NA	postposition-on-1-ma	NA
116	116) be envious of	NA	NA	NA	NA	trace-1-pon	NA	NA
117	117) be angry with	NA	postposition by-ba	NA	NA	NA	NA	NA
117	117) be angry with	NA	NA	postposition-on-1-ma	postposition-on-1-ma	postposition-on-1-ma	NA	NA
117	117) be angry with	side-3-fe	NA	NA	NA	NA	NA	NA
117	117) be angry with	NA	NA	NA	NA	NA	trace-1-pon	NA
117	117) be angry with	NA	NA	NA	NA	NA	NA	under-1-kodo
118	118) be surprised about	NA	NA	NA	NA	NA	NA	postposition at-la
118	118) be surprised about	postposition with-3-ga	NA	postposition with-3-ga	postposition with-3-ga	NA	NA	NA
118	118) be surprised about	NA	postposition-on-1-ma	NA	NA	NA	postposition-on-1-ma	NA
119	119) love smth	NA	NA	NA	NA	NA	NA	postposition with-2-yan
119	119) love smth	NA	NA	NA	postposition with-3-ga	NA	NA	NA
119	119) love smth	NA	postposition-for-2-len	NA	NA	NA	NA	NA
119	119) love smth	NA	NA	NA	NA	postposition-on-1-ma	postposition-on-1-ma	NA
120	120) enjoy/ take pleasure in	NA	postposition with-2-yan	NA	NA	NA	NA	NA
120	120) enjoy/ take pleasure in	NA	NA	postposition with-3-ga	NA	NA	NA	NA
121	121) want	NA	postposition by-ba	NA	NA	NA	NA	NA
121	121) want	NA	NA	NA	postposition with-3-ga	NA	NA	NA
121	121) want	NA	NA	postposition-on-1-ma	NA	NA	NA	NA
121	121) want	side-3-fe	NA	NA	side-3-fe	NA	NA	side-3-fe
121	121) want	NA	NA	NA	NA	trace-1-pon	trace-1-pon	NA
122	122) be angry with = 117?	NA	postposition by-ba	NA	NA	NA	NA	NA
122	122) be angry with = 117?	NA	NA	NA	NA	trace-1-pon	NA	NA
123	123) take offense by sb	before-dhie	NA	NA	NA	NA	NA	NA
123	123) take offense by sb	NA	NA	NA	NA	postposition in-2-sun	NA	NA
123	123) take offense by sb	NA	NA	NA	NA	NA	postposition with-3-ga	NA
123	123) take offense by sb	NA	postposition-on-1-ma	NA	NA	NA	NA	NA
124	124) make sad sb	NA	NA	NA	NA	NA	NA	postposition at-la
124	124) make sad sb	NA	NA	postposition-on-1-ma	NA	NA	NA	NA
124	124) make sad sb	NA	NA	NA	NA	trace-1-pon	NA	NA
125	125) be surprised by	NA	postposition-on-1-ma	NA	NA	postposition-on-1-ma	NA	NA
126	126) despise sb	NA	NA	NA	NA	postposition-on-1-ma	NA	postposition-on-1-ma
127	127) be sad because of sb	NA	NA	postposition by-ba	NA	NA	NA	NA
127	127) be sad because of sb	NA	NA	NA	postposition with-3-ga	NA	NA	NA
127	127) be sad because of sb	NA	postposition-on-1-ma	NA	NA	postposition-on-1-ma	NA	NA
127	127) be sad because of sb	NA	NA	NA	NA	NA	NA	side-3-fe
128	128) be annoyed by sb	NA	NA	NA	mouth-1-da	NA	NA	NA
128	128) be annoyed by sb	postposition-for-2-len	NA	NA	NA	NA	NA	NA
128	128) be annoyed by sb	NA	postposition-on-1-ma	NA	NA	NA	NA	NA
128	128) be annoyed by sb	NA	NA	NA	NA	trace-1-pon	NA	NA
129	129) sympathise to sb	NA	NA	NA	NA	NA	NA	postposition-on-1-ma
130	130) be embarrassed by sb	NA	NA	NA	0	NA	NA	NA
130	130) be embarrassed by sb	NA	NA	NA	postposition with-3-ga	NA	NA	NA
130	130) be embarrassed by sb	NA	NA	postposition-on-1-ma	NA	NA	postposition-on-1-ma	NA

Сложные глаголы

Если из двух компонентов совпал 1, мы ставим 0.5, если из трех 1, то 0.33, если из трех два, то 0.66. Если две конструкции, то учитываем максимальное совпадение

Code

complex_verbs <- readxl::read_xlsx("../GM_merged_wide_verb_meaning.xlsx")

complex_verbs |> 
  pivot_longer(names_to = "language", values_to = "verb_meaning", Guro:Kono) |> 
  filter(!is.na(verb_meaning)) |> 
  select(number, language, verb_meaning) |> 
  mutate(verb_meaning = str_remove_all(verb_meaning, "\\(.*?\\)"),
         verb_meaning = str_squish(verb_meaning),
         verb_meaning = str_split(verb_meaning, " ; ")) |>
  unnest_longer(verb_meaning) |> 
  separate_wider_delim(verb_meaning, delim = " ",
                       names = c("p1", "p2", "p3", "p4"),
                       too_few = "align_start") |> 
  filter(!is.na(p2)) |> 
  mutate(n_parts = case_when(!is.na(p4) ~ 4,
                             is.na(p4) & !is.na(p3) ~ 3,
                             is.na(p3) & !is.na(p2) ~ 2)) |> 
  pivot_longer(names_to = "p", values_to = "form", p1:p4) |> 
  mutate(form = case_when(str_detect(form, "\\?{1,3}") ~ NA,
                          TRUE ~ form)) |> 
  na.omit() |> 
  arrange(form, number) |> 
  mutate(id = str_c(number, "_", form),
         language = str_c(language, "_", n_parts)) |> 
  select(number, language, form) |> 
  arrange(number, language, form) ->
  analyze_df

map(sort(unique(analyze_df$number)), function(i){
  analyze_df |> 
    filter(number == i) |> 
    pairwise_count(language, form)  |> 
    mutate(total = case_when(str_detect(item1, "4") | str_detect(item2, "4") ~ 4,
                             str_detect(item1, "3") | str_detect(item2, "3") ~ 3, 
                             TRUE ~ 2),
           value = n/total,
           number = i,
           item1 = str_remove(item1, "_\\d"),
           item2 = str_remove(item2, "_\\d"))  |> 
    select(number, item1, item2, value, total) |> 
    filter(item1 != item2)
}) |> 
  list_rbind() ->
  result

result |> 
  group_by(item1, item2) |> 
  summarise(value = sum(value),
            total = sum(total)) |> 
  ggplot(aes(item1, item2))+
  geom_tile(aes(fill = value), colour = "white") +
  geom_text(aes(label = round(value, 3)), colour = "white") +
  scale_fill_gradient(low = "lightblue", high = "navy")+
  coord_fixed()+
  labs(x = "", y = "", title = "Heatmap with all stimuli") +
  theme(legend.position = "bottom")

Чем выше значение, тем больше сходство. У меня есть подозрение, что я где-то налажал (ну и я сейчас выкинул все, что я подозревал в ошибке), но оно говорит что-то не совсем дикое: огромное сходство кпелле и коно, большое сходство кпелле и мано… Чудеса…

Я преобразую все вот в такую таблицу. К language я привинчиваю количество частей. Так что, например, первые две строчки нужно читать так: в стимуле номер 1 в языке Dan_Gweetaa 2 части break и interior. Так как таблица отсортирована по номеру стимула, то больше сложных глаголов в первом стимуле нет.

Я сделал некоторый хак, который позволяет делать кластеризацию. Я завожу признаки, которые состоят из фрагмента значения и номера, в котором он встречается:

Code

analyze_df  |> 
  mutate(form = str_c(form, "_", number),
         language = str_remove(language, "_\\d")) |> 
  distinct(language, form) |> 
  mutate(value = 1) |> 
  pivot_wider(names_from = language, values_from = value, values_fill = 0)

form	Dan_Gweetaa	Mano	Guro	Kpelle	Bamana	Kono	Looma
break_1	1	0	0	0	0	0	0
interior_1	1	0	0	0	0	0	0
frighten_3	0	1	0	0	0	0	0
prev_3	0	1	0	0	0	0	0
belief_7	1	0	0	0	0	0	0
implement_7	1	0	0	0	0	0	0
heart_7	0	1	1	0	0	0	0
put_in_7	0	0	1	0	0	0	0
put_on_7	0	0	1	0	0	0	0
install_7	0	1	0	1	0	0	0
spirit_7	0	0	0	1	0	0	0
mouth_10	0	0	0	0	1	0	0
press_10	0	0	0	0	1	0	0
put_on_10	0	0	1	0	0	0	0
work_10	0	0	1	0	0	0	0
surface_10	0	0	1	0	0	0	0
gain_13	0	0	0	0	1	0	0
power_13	0	0	0	0	1	0	0
bone_13	1	0	0	0	0	0	0
do_13	1	0	0	0	0	0	0
head_13	1	0	0	0	0	0	0
take_13	1	0	0	0	0	0	0
hand_13	0	1	0	1	0	0	0
raise_13	0	0	0	1	0	0	0
be_able_13	0	1	0	0	0	0	0
prev_16	0	0	0	1	0	0	0
stretch_16	0	0	0	1	0	0	0
be_18	0	1	0	0	0	0	0
hand_18	0	1	0	0	0	0	0
breast_20	0	0	0	1	0	1	1
milk_20	0	1	0	1	0	1	0
remove_20	0	1	0	1	0	1	1
cold_20	0	0	0	0	0	0	1
gain_21	0	0	0	1	0	0	0
prev_21	0	0	0	1	0	0	0
implement_21	0	1	0	0	0	0	0
hand_22	0	1	0	1	0	0	0
sit_22	0	0	0	1	0	0	0
stretch_22	0	0	0	1	0	0	0
touch_22	0	1	0	0	0	0	0
fight_23	1	1	1	1	0	0	0
wage_23	1	0	0	0	0	0	0
sell_23	0	0	1	0	0	0	0
battle_23	0	1	0	1	0	0	0
be_24	0	0	1	0	0	0	0
friend_24	0	0	1	0	0	0	0
do_24	0	1	0	0	0	0	0
friendship_24	0	1	0	0	0	0	0
do_25	1	0	0	0	0	0	0
time_25	1	0	0	0	0	0	0
interior_25	0	0	1	0	0	0	0
remember_25	0	0	1	0	0	0	0
stomach_25	0	0	1	0	0	0	0
spirit_25	0	1	0	1	0	1	0
surface_25	0	0	0	1	0	1	0
walk_25	0	1	0	1	0	1	0
mind_25	0	0	0	0	0	0	1
sit_25	0	0	0	0	0	0	1
come_25	0	1	0	0	0	0	0
memory_25	0	1	0	0	0	0	0
in_front_of_25	0	1	0	0	0	0	0
eye_28	1	0	0	0	0	0	0
leave_28	1	0	0	0	0	0	0
in_front_of_28	0	0	1	0	0	0	0
wait_28	0	0	1	0	0	0	0
prev_28	0	1	0	1	0	1	0
unite_28	0	1	0	1	0	1	0
appear_29	1	0	0	0	0	0	0
heart_29	1	1	0	0	0	0	0
be_mistaken_29	0	0	1	0	0	0	0
interior_29	0	0	1	0	0	0	0
forget_29	0	1	0	0	0	0	0
call_31	0	1	0	0	0	0	0
do_31	0	1	0	0	0	0	0
implement_35	0	0	0	1	0	0	0
lie_35	0	0	0	1	0	0	1
prev_35	0	1	0	1	0	0	0
surface_35	0	0	0	0	0	0	1
back_35	0	1	0	0	0	0	0
put_in_35	0	1	0	0	0	0	0
put_on_35	0	1	0	0	0	0	0
do_36	1	1	0	0	0	0	0
thing:CSTR_36	1	0	0	0	0	0	0
thing_36	0	1	0	0	0	0	0
mockery_37	1	0	0	0	1	0	0
remove_37	0	0	0	0	1	0	1
tithe_37	0	0	0	0	1	0	0
install_37	1	0	0	0	0	0	0
split_37	1	0	0	0	0	0	0
laughter_37	0	0	0	0	0	0	1
eye_39	0	1	0	0	0	0	0
put_on_39	0	1	0	0	0	0	0
walk_39	0	1	0	0	0	0	0
kaolin_40	0	0	1	0	0	0	0
spread_40	0	0	1	0	0	0	0
water_40	0	0	1	0	0	0	0
go_up_40	0	0	0	1	0	1	0
prev_40	0	0	0	0	0	1	0
painting_40	0	0	0	1	0	0	0
install_41	0	1	1	0	0	0	0
tooth_41	0	1	1	0	0	0	0
break_44	0	0	0	1	0	1	0
interior_44	0	0	0	1	0	1	0
do_45	1	0	0	0	0	0	0
falsehood_45	1	0	0	0	0	0	0
praise_45	0	0	0	0	0	1	0
prev_45	0	1	0	0	0	1	0
go_up_45	0	0	0	1	0	0	0
prev2_45	0	0	0	1	0	0	0
monter_45	0	1	0	0	0	0	0
do_46	1	1	0	0	0	0	0
love_46	1	1	0	1	0	1	0
accompany_46	0	0	1	0	0	0	0
heart_46	0	0	1	0	0	0	0
be_46	0	1	0	1	0	1	0
face_46	0	1	0	0	0	0	0
matter_46	0	1	0	0	0	0	0
remove_46	0	1	0	0	0	0	0
implement_47	0	1	0	0	0	0	0
movement_47	0	1	0	0	0	0	0
dream_48	0	1	0	1	0	1	0
implement_48	0	0	0	1	0	1	0
kill_48	0	1	0	0	0	0	0
interior_49	0	0	0	0	0	1	0
wash_49	0	1	0	1	0	1	0
prev_49	0	1	0	1	0	0	0
be_51	0	1	0	1	0	1	0
name_51	0	1	0	1	0	1	0
lie_52	1	0	0	0	0	0	1
matter_52	1	0	1	0	0	0	0
in_front_of_52	0	0	1	0	0	0	0
install_52	0	0	1	0	0	0	0
law_52	0	0	0	0	0	0	1
fill_54	0	1	0	1	0	1	0
mouth_54	0	1	0	1	0	1	1
interior_54	0	1	0	0	0	0	0
completeness_56	0	1	0	0	0	0	0
cop.NEG_56	0	1	0	0	0	0	0
do_57	1	0	0	0	0	0	0
repugnance_57	1	0	0	0	0	0	0
boil_57	0	0	1	0	0	0	0
heart_57	0	0	1	0	0	0	0
be.neg_57	0	1	0	1	0	1	0
love_57	0	1	0	1	0	1	0
end_57	0	0	0	1	0	0	0
take_57	0	0	0	0	0	0	1
do_58	1	0	0	0	0	0	0
love_58	1	0	0	0	0	1	0
be_58	0	0	0	0	0	1	0
catch_58	0	1	0	1	0	0	0
heart_58	0	1	0	1	0	0	0
drop_58	0	1	0	0	0	0	0
catch_59	1	0	0	0	0	0	0
need_59	1	1	0	0	0	0	0
be_59	0	1	0	1	0	1	0
prev-issue_59	0	0	0	0	0	1	0
prev-be_obliged_59	0	0	0	1	0	0	0
prev_60	0	1	0	1	0	0	0
turn_60	0	1	0	1	0	0	0
answer_62	1	0	0	0	0	0	0
appear_62	1	0	0	0	0	0	0
question_62	1	0	0	0	0	0	0
implement_62	0	0	0	0	0	1	0
back_62	0	0	0	1	0	0	0
prev2_62	0	0	0	1	0	0	0
return_62	0	0	0	1	0	0	0
speech_62	0	1	0	1	0	0	0
accept_62	0	1	0	0	0	0	0
interior_62	0	1	0	0	0	0	0
remove_62	0	1	0	0	0	0	0
surface_62	0	1	0	0	0	0	0
turn_62	0	1	0	0	0	0	0
board_63	0	0	0	0	1	0	0
open_63	0	0	0	0	1	0	0
enlever_63	0	0	1	0	0	0	0
in_front_of_63	0	0	1	0	0	0	0
implement_63	0	0	0	1	0	1	0
mouth_63	0	1	0	1	0	1	1
stand_63	0	0	0	0	0	0	1
remove_63	0	1	0	0	0	0	0
be_64	0	1	0	0	0	0	0
différent_64	0	1	0	0	0	0	0
do_66	1	0	0	0	0	0	0
field_66	1	0	0	0	0	1	0
make_66	0	0	0	0	0	1	0
cultivate_66	0	0	0	1	0	0	0
place_66	0	1	0	1	0	0	0
remove_66	0	1	0	0	0	0	0
install_67	0	0	1	0	0	0	0
smell_67	0	1	1	1	0	1	0
surface_67	0	0	1	0	0	0	0
be_67	0	1	0	1	0	1	0
cross_68	0	0	0	0	0	1	0
interior_68	0	0	0	0	0	1	0
pass_68	0	0	0	0	0	0	1
surface_68	0	0	0	0	0	0	1
cut_68	0	1	0	0	0	0	0
remove_69	1	0	0	0	0	0	0
song_69	1	1	0	1	0	1	1
fall_69	0	0	0	1	0	1	0
drop_69	0	0	0	0	0	0	1
implement_69	0	1	0	0	0	0	0
kill_70	1	0	0	0	0	0	0
writing_70	1	0	0	0	0	0	0
drop_72	0	0	1	0	0	0	0
water_72	0	0	1	0	0	0	0
encounter_73	0	0	0	0	1	0	0
measure_73	0	0	0	0	1	0	0
catch_73	0	0	0	1	0	0	0
prev_73	0	0	0	1	0	0	0
cover_75	0	0	0	0	1	0	0
mouth_75	0	0	0	0	1	0	0
in_front_of_75	0	0	1	0	0	0	0
unite_75	0	0	1	0	0	0	0
lie_75	0	0	0	0	0	1	0
stretch_75	0	0	0	0	0	1	0
close_75	0	0	0	1	0	0	0
upper_surface_75	0	0	0	1	0	0	0
descend_76	0	0	0	0	1	0	0
reason_76	0	0	0	0	1	0	0
heart_76	1	1	0	0	0	0	0
wake_76	1	1	0	0	0	0	0
spirit_76	0	0	0	1	0	1	0
surface_76	0	0	0	0	0	1	0
walk_76	0	0	0	0	0	1	0
be_76	0	0	0	1	0	0	0
mind_76	0	0	0	0	0	0	1
sit_76	0	0	0	0	0	0	1
come_76	0	1	0	0	0	0	0
memory_76	0	1	0	0	0	0	0
take_76	0	1	0	0	0	0	0
catch_77	1	0	0	0	0	0	0
surface_77	1	0	0	0	0	0	0
give_77	0	0	0	1	0	1	0
help_77	0	0	0	1	0	1	0
implement_77	0	0	0	1	0	0	0
prev_77	0	1	0	1	0	0	0
prev-help_77	0	0	0	1	0	0	0
unite_77	0	1	0	0	0	0	0
hear_78	1	0	0	1	0	0	1
inside_78	1	0	0	0	0	0	0
speech_78	0	0	0	1	0	0	0
voice_78	0	0	0	0	0	0	1
collect_82	0	0	0	0	1	0	0
defeat_82	0	0	0	0	1	0	0
be_able_82	0	1	0	1	0	0	0
hand_82	0	1	0	1	0	0	0
in_83	0	0	0	0	1	0	0
please_83	0	0	0	0	1	0	0
soul_83	0	0	0	0	1	0	0
catch_83	1	0	0	0	0	0	0
do_83	1	0	0	0	0	0	0
heart_83	1	0	0	0	0	0	0
satisfaction_83	1	0	0	0	0	0	0
become_happy_83	0	0	1	0	0	0	0
interior_83	0	0	1	0	0	0	0
be_nice_83	0	0	0	1	0	1	1
stomach.loc_83	0	0	0	1	0	1	1
become_beautiful_83	0	1	0	0	0	0	0
stomach_83	0	1	0	0	0	0	0
kill_84	1	0	0	0	0	0	0
talk_84	1	0	0	1	0	1	0
install_84	0	1	0	1	0	1	0
do_84	0	0	0	1	0	0	0
implement_84	0	1	0	1	0	0	0
chat_84	0	1	0	0	0	0	0
jugement_84	0	1	0	0	0	0	0
descend_85	0	0	0	1	0	1	0
mouth_85	0	0	0	1	0	1	0
implement_87	0	0	0	1	0	0	0
prev2-speech_87	0	0	0	1	0	0	0
surface_87	0	1	0	0	0	0	0
take_87	0	1	0	0	0	0	0
interior_87	0	1	0	0	0	0	0
say_87	0	1	0	0	0	0	0
speech_87	0	1	0	0	0	0	0
heart_88	1	0	0	0	0	0	0
implement_88	1	0	0	0	0	0	0
catch_88	0	1	0	1	0	1	0
love_88	0	1	0	1	0	1	0
behind_89	0	1	0	0	0	0	0
chase_89	0	1	0	0	0	0	0
down_90	1	0	0	0	0	0	0
go_90	1	0	0	0	0	0	0
ear_91	1	1	0	1	1	1	1
prev-stand_91	0	0	0	0	1	0	0
leave_91	1	0	0	0	0	0	0
install_91	0	1	0	1	0	1	0
hear_91	0	0	0	1	0	0	0
speech_91	0	0	0	1	0	0	0
stand_91	0	0	0	0	0	0	1
respect_92	1	0	0	0	0	0	0
sit_92	1	0	0	0	0	0	0
hear_93	0	0	0	1	0	0	0
speech_93	0	1	0	1	0	0	0
ear_93	0	0	0	0	0	0	1
stand_93	0	0	0	0	0	0	1
entendre_93	0	1	0	0	0	0	0
finish_95	0	1	0	1	0	0	0
prev_95	0	1	0	1	0	0	0
dream_97	1	1	0	1	0	1	0
remove_97	1	0	0	0	0	0	0
implement_97	0	0	0	1	0	1	0
kill_97	0	1	0	0	0	0	0
lie_98	0	0	0	0	0	0	1
speech_98	0	1	0	0	0	0	1
install_98	0	1	0	0	0	0	0
put_99	1	0	0	0	0	0	0
quarrel_99	1	0	0	0	0	0	0
make_99	0	0	0	1	0	1	0
work_99	0	0	0	0	0	1	0
argument_99	0	0	0	1	0	0	0
do_99	0	1	0	0	0	0	0
impertinence_99	0	1	0	0	0	0	0
install_99	0	1	0	0	0	0	0
speech_99	0	1	0	0	0	0	0
hit_101	0	0	0	0	1	0	0
powder_101	0	0	0	0	1	0	0
go.up_101	1	0	0	0	0	0	0
gun_101	1	0	0	1	0	0	1
bow_101	0	0	1	0	0	0	0
throw_101	0	0	1	0	0	0	0
fall_101	0	0	0	1	0	0	0
drop_101	0	1	0	0	0	0	1
fusil_101	0	1	0	0	0	0	0
implement_102	0	0	0	0	0	1	0
mouth_107	0	0	0	0	1	1	1
smell_107	0	0	0	0	1	0	0
enter_107	0	0	0	0	0	1	0
touch_107	0	0	0	0	0	0	1
say_108	1	0	0	0	0	0	0
voice_108	1	0	0	0	0	0	0
dire_108	0	0	1	0	0	0	0
surface_108	0	0	1	0	0	0	0
book_108	0	0	0	1	0	0	0
implement_108	0	0	0	1	0	0	0
implement_109	0	1	0	0	0	0	0
mouvement_109	0	1	0	0	0	0	0
be_110	1	0	0	0	0	0	0
respect_110	1	1	0	0	0	0	0
be_heavy_110	0	0	0	1	0	1	0
prev_110	0	1	0	1	0	1	0
give_110	0	1	0	0	0	0	1
head_110	0	0	0	0	0	0	1
sit_110	0	1	0	0	0	0	0
do_110	0	1	0	0	0	0	0
heavy_110	0	1	0	0	0	0	0
appear_111	1	0	0	0	0	0	0
chase_111	1	0	0	0	0	0	0
disgust_111	1	0	0	0	0	0	0
accompany_111	0	0	1	0	0	0	0
be.neg_111	0	0	0	0	0	1	0
love_111	0	0	0	0	0	1	0
drop_111	0	1	0	0	0	0	0
eye_111	0	1	0	0	0	0	0
implement_111	0	1	0	0	0	0	0
neglect_111	0	1	0	0	0	0	0
respect_111	0	1	0	0	0	0	0
spoil_111	0	1	0	0	0	0	0
eye_112	0	0	0	0	1	0	0
fill_112	0	0	0	0	1	0	0
be_nice_112	0	0	0	0	0	1	1
stomach.loc_112	0	0	0	0	0	1	1
be_good_112	0	0	0	1	0	0	0
stomach_112	0	1	0	1	0	0	0
become_beautiful_112	0	1	0	0	0	0	0
go_up_113	1	0	0	0	0	0	0
love_113	1	1	0	1	0	1	0
fall_113	0	0	0	0	0	1	0
drop_113	0	1	0	1	0	0	0
do_115	1	1	0	0	0	0	0
pity_115	1	1	0	0	0	0	0
see_115	0	0	0	0	0	1	0
be_115	0	1	0	1	0	0	0
eye_115	0	0	0	1	0	0	0
prev-love_115	0	0	0	1	0	0	0
catch_115	0	0	0	0	0	0	1
pain_115	0	0	0	0	0	0	1
surface_115	0	0	0	0	0	0	1
give_birth_115	0	1	0	0	0	0	0
pitiful_115	0	1	0	0	0	0	0
do_116	1	0	0	0	0	0	0
jealousy_116	1	0	0	0	0	0	0
jelousy_116	0	0	1	0	0	0	0
wear_116	0	0	1	0	0	0	0
cut_116	0	1	0	0	0	0	0
heart_116	0	1	0	0	0	0	0
anger_117	1	0	0	0	0	0	0
take_117	1	0	0	0	0	0	0
boil_117	0	0	1	0	0	0	0
heart_117	0	0	1	1	0	1	1
sting_117	0	0	0	1	0	1	0
be_bitter_117	0	0	0	0	0	0	1
attach_117	0	1	0	0	0	0	0
face_117	0	1	0	0	0	0	0
ball_117	0	1	0	0	0	0	0
do_118	1	0	0	0	0	0	0
matter_118	1	0	0	0	0	0	0
surprise_118	1	0	0	0	0	0	0
with_118	1	0	0	0	0	0	0
envelop_118	0	0	1	0	0	0	0
interior_118	0	0	1	0	0	0	0
on.it_118	0	0	0	1	0	0	0
stomach.loc_118	0	0	0	1	0	0	0
throw_118	0	1	0	1	0	0	0
be_dry_118	0	0	0	0	0	0	1
mouth_118	0	0	0	0	0	0	1
stomach_118	0	1	0	0	0	0	0
do_119	1	0	0	0	0	0	0
love_119	1	0	0	1	0	1	0
be_119	0	0	0	1	0	1	0
profit_120	0	0	0	0	1	0	0
remove_120	0	0	0	0	1	0	1
see_120	1	0	0	0	0	0	0
taste_120	1	1	0	0	0	0	0
become_happy_120	0	0	1	0	0	0	0
interior_120	0	0	1	0	0	0	0
be_good_120	0	0	0	1	0	0	0
stomach_120	0	0	0	1	0	0	0
pleasure_120	0	0	0	0	0	0	1
take_120	0	1	0	0	0	0	0
do_121	1	0	0	0	0	0	0
love_121	1	0	0	0	0	0	0
become_happy_121	0	0	1	0	0	0	0
interior_121	0	0	1	0	0	0	0
boil_122	0	0	1	0	0	0	0
heart_122	0	0	1	0	0	1	0
sting_122	0	0	0	0	0	1	0
aggravate_123	0	0	1	0	0	0	0
speech_123	0	1	1	0	0	0	0
implement_123	0	0	0	0	0	1	0
passage_123	0	0	0	0	0	1	0
character_123	0	0	0	1	0	0	0
make_123	0	0	0	1	0	0	0
interior_123	0	1	0	0	0	0	0
search_123	0	1	0	0	0	0	0
enter_124	0	0	0	0	1	0	0
guilt_124	0	0	0	0	1	0	0
heart_124	1	0	0	0	0	0	0
wound_124	1	0	0	0	0	0	0
finish_124	0	0	0	0	0	1	0
face_124	0	0	0	1	0	0	0
kill_124	0	0	0	1	0	0	0
pain_124	0	0	0	0	0	0	1
pass_124	0	0	0	0	0	0	1
remove_124	0	0	0	0	0	0	1
do_124	0	1	0	0	0	0	0
pitoyable_124	0	1	0	0	0	0	0
end_125	0	0	0	0	1	0	0
means_125	0	0	0	0	1	0	0
envelop_125	0	0	1	0	0	0	0
interior_125	0	0	1	0	0	0	0
on.it_125	0	0	0	0	0	1	0
stomach.loc_125	0	0	0	0	0	1	0
throw_125	0	0	0	0	0	1	0
do_126	1	0	0	0	0	0	0
scorn_126	1	0	0	0	0	0	0
implement_126	0	0	0	1	0	1	0
insult_126	0	0	0	0	0	1	0
pass_126	0	0	0	1	0	0	0
install_126	0	1	0	0	0	0	0
kill_126	0	1	0	0	0	0	0
cry_127	0	0	0	0	1	0	0
heart_127	0	0	0	0	1	0	0
fire_127	0	0	1	0	0	0	0
install_127	0	0	1	0	0	0	0
interior_127	0	0	1	0	0	0	0
be_nice_127	0	0	0	0	0	1	0
stomach.loc_127	0	0	0	0	0	1	0
pass_127	0	0	0	0	0	0	1
body_127	0	1	0	0	0	0	0
break_127	0	1	0	0	0	0	0
fill_127	0	1	0	0	0	0	0
mouth_127	0	1	0	0	0	0	0
boil_128	0	0	1	0	0	0	0
heart_128	0	0	1	0	0	1	0
sting_128	0	0	0	0	0	1	0
implement_129	0	0	0	1	0	1	0
sit_129	0	0	0	0	0	1	0
prev_129	0	0	0	1	0	0	0
prev-help_129	0	0	0	1	0	0	0
inside_130	1	0	0	0	0	0	0
obey_130	1	0	0	0	0	0	0
cold_130	0	0	0	0	0	0	1
heart_130	0	0	0	0	0	0	1

Code

analyze_df  |> 
  mutate(form = str_c(form, "_", number),
         language = str_remove(language, "_\\d")) |> 
  distinct(language, form) |> 
  mutate(value = 1) |> 
  pivot_wider(names_from = language, values_from = value, values_fill = 0) |> 
  column_to_rownames("form") |>
  t() |> 
  dist(method = "binary") ->
  complex_verbs_distance

hclust

Code

complex_verbs_distance |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Complex verbs semantics (all stimuli)",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

neighbornet

Code

complex_verbs_distance  |> 
  neighborNet() |> 
  plot()
title(main = "Complex verbs semantics (all stimuli)")

Конечно, при таком варианте получается много единичных случаев, которые удлинняют палки и в кластеризации и в нейборнете, однако таких случаев, когда во всех семи языках есть форма.

Что если посмотреть только на те стимулы, для которых есть какая-то форма (сложный глагол или нет):

Code

analyze_df  |>
  filter(number %in% to_keep) |> 
  mutate(form = str_c(form, "_", number),
         language = str_remove(language, "_\\d")) |> 
  distinct(language, form) |> 
  mutate(value = 1) |> 
  pivot_wider(names_from = language, values_from = value, values_fill = 0) |> 
  column_to_rownames("form") |>
  t() |> 
  dist(method = "binary") ->
  complex_verbs_distance_subset

hclust

Code

complex_verbs_distance_subset |> 
  hclust() |> 
  as.phylo()  %>%
  plot(main = "Complex verbs semantics with common stimuli",
       tip.color = colors$color[match(.$tip.label, colors$language)],
       direction = "downwards",
       cex = 1.5, 
       font = 2)

neighbornet

Code

complex_verbs_distance_subset  |> 
  neighborNet() |> 
  plot()
title(main = "Complex verbs semantics with common stimuli")

Карта

Code

library(lingtypology)
readxl::read_xlsx("../for_map.xlsx") |> 
  mutate(language_name = lang.gltc(glottocode)) ->
  for_map

map.feature(languages = for_map$language_name,
            latitude = for_map$latitude,
            longitude = for_map$longitude,
            color = "black", 
            width = 6) |> 
  map.feature(languages = for_map$language_name,
            latitude = for_map$latitude,
            longitude = for_map$longitude,
            features = for_map$color,
            color = for_map$color, 
            legend = FALSE,
            tile = "Esri.WorldGrayCanvas",
            label = for_map$language,
            label.position = "bottom", 
            label.hide = FALSE,
            minimap = TRUE,
            pipe.data = _)

Preprocessing

Code

library(tidyverse)
mano_kpelle_pre <- readxl::read_xlsx("../say_stimuli.xlsx", sheet = "mano + kpelle")

mano_kpelle_pre |> 
  rename(number = Number,
         construction = `Mano construction`,
         construction_type = `Mano extended construction type`,
         adposition = `Mano Postposition`,
         verb = `Mano verb`,
         verb_meaning = `Mano Complex verb meaning`) |> 
  mutate(language = "Mano") |> 
  select(number, construction, construction_type, adposition, verb, verb_meaning, language) |> 
  filter(!is.na(construction)) ->
  mano

mano_kpelle_pre |> 
  rename(number = Number,
         construction = `Kpelle construction`,
         construction_type = `Kpelle extended construction type`,
         adposition = `Kpelle postposition`,
         verb = `Kpelle verb`,
         verb_meaning = `Kpelle complex verb meaning`) |> 
  mutate(language = "Kpelle") |> 
  select(number, construction, construction_type, adposition, verb, verb_meaning, language) |> 
  filter(!is.na(construction)) ->
  kpelle

rm(mano_kpelle_pre)

kono_pre <- readxl::read_xlsx("../say_stimuli.xlsx", sheet = "kono")

kono_pre |> 
  rename(number = Number,
         construction = `Kono construction...8`,
         construction_type = `Kono extended construction`,
         adposition = `Kono adposition`,
         verb = `Kono verb`,
         verb_meaning = `Kono complex verb`) |> 
  mutate(language = "Kono") |> 
  select(number, construction, construction_type, adposition, verb, verb_meaning, language) |> 
  filter(!is.na(construction)) ->
  kono

rm(kono_pre)

# I renamed one of the columns to "Looma extended construction"
looma_pre <- readxl::read_xlsx("../say_stimuli.xlsx", sheet = "Looma")

looma_pre |> 
  rename(number = Number,
         construction = `Looma construction`,
         construction_type = `Looma extended construction`,
         adposition = `Looma Postposition`,
         verb = `Looma Verb`,
         verb_meaning = `Looma Complex Verb`) |> 
  mutate(language = "Looma") |> 
  select(number, construction, construction_type, adposition, verb, verb_meaning, language) |> 
  filter(!is.na(construction)) ->
  looma

rm(looma_pre)

# I renamed one of the columns to "Guro extended construction"
guro_pre <- readxl::read_xlsx("../say_stimuli.xlsx", sheet = "Guro")

guro_pre |> 
  rename(number = Number,
         construction = `Guro construction`,
         construction_type = `Guro extended construction`,
         adposition = `Guro Postposition`,
         verb = `Guro Verb`,
         verb_meaning = `Guro Complex Verb`) |> 
  mutate(language = "Guro") |> 
  select(number, construction, construction_type, adposition, verb, verb_meaning, language) |> 
  filter(!is.na(construction)) ->
  guro

rm(guro_pre)

dan_pre <- readxl::read_xlsx("../say_stimuli.xlsx", sheet = "Dan Gweetaa from Vydrin")

dan_pre |> 
  rename(number = Number,
         construction = `Dan construction`,
         construction_type = `Dan extended construction type`,
         adposition = `Dan Postposition`,
         verb = `Dan Verb`, 
         verb_meaning = `Dan complex verb meaning`) |> 
  mutate(language = "Dan_Gweetaa") |> 
  select(number, construction, construction_type, adposition, verb, verb_meaning, language) |> 
  filter(!is.na(construction)) ->
  dan

rm(dan_pre)

bamana_pre <- readxl::read_xlsx("../say_stimuli.xlsx", sheet = "Bambara Vydrin")
bamana_pre |> 
  rename(number = Number,
         construction = `Bamana construction`,
         construction_type = `Bamana extended construction type`,
         adposition = `Bamana Postposition`,
         verb = `Bamana Verb`, 
         verb_meaning = `Bamana complex verb meaning`) |> 
  mutate(language = "Bamana") |> 
  select(number, construction, construction_type, adposition, verb, verb_meaning, language) |> 
  filter(!is.na(construction)) ->
  bamana

guro |> 
  bind_rows(kono, kpelle, looma, mano, dan, bamana) |>
  mutate(construction_type = str_replace_all(construction_type, " N N ", " N ")) |>
  write_csv("../GM_merged.csv", na = "")

readxl::read_xlsx("../say_stimuli.xlsx", sheet = "stimuli_general") |> 
  select(Number, `English predicate`, `Stimulus sentence (SAY)`, `Semantic types`) |> 
  rename(number = Number,
         predicate_eng = `English predicate`,
         stimuli = `Stimulus sentence (SAY)`,
         semantic_type = `Semantic types`) ->
  stimuli

read_csv("../GM_merged.csv") |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type)) |>
  group_by(number) |> 
  add_count(construction_type) |> 
  arrange(number, desc(n)) |> 
  select(-adposition) |> 
  group_by(number, construction_type, language, n) |> 
  reframe(construction = str_c(construction, collapse = "; ")) |> 
  pivot_wider(names_from = language, values_from = construction, values_fill = "") |> 
  group_by(number) |> 
  mutate(id = 1:n()) |>
  ungroup() |> 
  left_join(stimuli) |> 
  select(number, semantic_type, predicate_eng, stimuli, id, n, construction_type, Guro, Looma, Mano, Dan_Gweetaa, Kono, Kpelle, Bamana, Kono) |> 
  writexl::write_xlsx("../GM_merged_wide.xlsx")

read_csv("../GM_merged.csv") |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type)) |>
  select(-construction) |>
  distinct(number, construction_type, language, adposition) |> 
  group_by(number, construction_type, language) |> 
  reframe(adposition = str_c(adposition, collapse = "; ")) |> 
  pivot_wider(names_from = language, values_from = adposition, values_fill = "") |> 
  group_by(number) |> 
  mutate(id = 1:n()) |>
  ungroup() |> 
  left_join(stimuli) |> 
  select(number, semantic_type, predicate_eng, stimuli, id, construction_type, Guro, Looma, Mano, Dan_Gweetaa, Kono, Kpelle, Bamana, Kono) |> 
  writexl::write_xlsx("../GM_merged_wide_adpositions.xlsx")

read_csv("../GM_merged.csv") |> 
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type)) |>
  select(-construction) |>
  distinct(number, construction_type, language, verb) |> 
  group_by(number, construction_type, language) |> 
  reframe(verb = str_c(verb, collapse = "; ")) |> 
  pivot_wider(names_from = language, values_from = verb, values_fill = "") |> 
  group_by(number) |> 
  mutate(id = 1:n()) |>
  ungroup() |> 
  left_join(stimuli) |> 
  select(number, predicate_eng, stimuli, id, construction_type, Guro, Looma, Mano, Dan_Gweetaa, Kpelle, Bamana, Kono) |> 
  writexl::write_xlsx("../GM_merged_wide_verb.xlsx")

read_csv("../GM_merged.csv") |>
  filter(!str_detect(construction, "no equivalent"),
         !is.na(construction_type),
         !is.na(verb_meaning)) |>
  select(-construction) |>
  distinct(number, construction_type, language, verb, verb_meaning) |> 
  mutate(verb_meaning = str_c(verb_meaning, " (", verb, ")")) |> 
  group_by(number, construction_type, language) |>
  reframe(verb_meaning = str_c(verb_meaning, collapse = "; ")) |>
  pivot_wider(names_from = language, values_from = verb_meaning, values_fill = "") |>
  group_by(number) |>
  mutate(id = 1:n()) |>
  ungroup() |>
  left_join(stimuli) |>
  select(number, predicate_eng, stimuli, id, construction_type, Guro, Looma, Mano, Dan_Gweetaa, Kpelle, Bamana, Kono) |>
  writexl::write_xlsx("../GM_merged_wide_verb_meaning.xlsx")